ArticlePDF AvailableLiterature Review

Machine learning in the prediction of cancer therapy

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Abstract and Figures

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.
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Review
Machine learning in the prediction of cancer therapy
Raihan Rafique
a,1
, S.M. Riazul Islam
b,1
, Julhash U. Kazi
c,d,
a
Ideflod AB, Lund, Sweden
b
Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
c
Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
d
Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
article info
Article history:
Received 14 March 2021
Received in revised form 6 July 2021
Accepted 7 July 2021
Available online 08 July 2021
Keywords:
Artificial intelligence
Deep learning
Monotherapy prediction
Drug combinations
Drug synergy
Variational autoencoder
Restricted Boltzmann machine
Support vector machines
Ridge regression
Elastic net
Lasso
Random forests
Deep neural network
Convolutional neural network
Graph convolutional network
Matrix factorization
Factorization machine
Higher-order factorization machines
Visible neural network
Ordinary differential equation
abstract
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many
cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning.
The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic muta-
tions. Evidently, the presence of a genetic mutation does not always predict the therapeutic response
and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to
match a cancer patient with a specific drug or drug combination. Recent advancements in predictive
models using artificial intelligence have shown great promise in preclinical settings. However, despite
massive improvements in computational power, building clinically useable models remains challenging
due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of
recent advancements in therapeutic response prediction using machine learning, which is the most
widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illus-
trate their use, and highlight the current challenges in therapy response prediction for clinical practice.
Ó2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and
Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.
org/licenses/by/4.0/).
Contents
1. Introduction . . . ..................................................................................................... 4004
2. Basics of therapy response prediction. . .................................................................................. 4004
2.1. Pharmacogenomic data resources . . . . . . . . . .... .................................................................... 4004
2.2. Data preprocessing . . . . . .... .................................................................................. .. 4005
3. ML algorithms for drug response prediction . . . . . . . . . . . . . . . ............................................................... 4005
3.1. Linear regression . . . . . . . ............................................................................. ........... 4005
3.2. Nonlinear regression . . . . ................ ........................................................................ 4006
3.3. Kernel functions . . . . . . . ................................... ..................................................... 4006
3.4. Deep learning . . . . . . . . . ....................... ................................................................. 4008
https://doi.org/10.1016/j.csbj.2021.07.003
2001-0370/Ó2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Corresponding author at: Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon village Building 404:C3, Scheelevägen
8, 22363 Lund, Sweden.
E-mail address: kazi.uddin@med.lu.se (J.U. Kazi).
1
Equal contributions.
Computational and Structural Biotechnology Journal 19 (2021) 4003–4017
journal homepage: www.elsevier.com/locate/csbj
4. Monotherapy response prediction. . . . . .................................................................................. 4008
4.1. Classical ML models in monotherapy prediction. . . . . . . . . . . . . . . . . ...................................... ............... 4009
4.2. Deep neural networks in monotherapy prediction . . . . . . . . . . . . . . . ............................................. ........ 4010
4.3. Matrix factorization and factorization machines in monotherapy prediction . . . . . . . . . . . . . .... .............................. 4010
4.4. Autoencoders in monotherapy prediction . . . ............................................................. ........... 4010
4.5. Graph convolutional networks in monotherapy prediction . . . . . . . . .......... ........................................... 4011
4.6. Visible neural networks in monotherapy prediction. . . . . . . . . . . . . . ...................................... ............... 4011
4.7. PDXs and organoids in monotherapy prediction. . . . . . . . . . . . . . . . . ................................... .................. 4011
5. Drug synergy prediction . . . . . . . . . . . . .................................................................................. 4011
5.1. Drug synergy prediction using conventional ML methods . . . . . . . . . ..................................................... 4012
5.2. Drug synergy prediction using DL . . . . . . . . . ................................... ..................................... 4012
5.3. Synergy prediction with a higher-order factorization machine . . . . . ................................ ..................... 4013
5.4. Synergy prediction using an autoencoder . . . ............................. ........................................... 4013
5.5. Synergy prediction with a graph convolutional network . . . . . . . . . . ................................ ..................... 4013
5.6. Restricted Boltzmann machine for predicting drug synergy . . . . . . . ....... .............................................. 4013
6. Limitations in the development of clinically relevant predictive models . . . . . . . . . . . . ............................................ 4013
7. Conclusion . . . . ..................................................................................................... 4014
CRediT authorship contribution statement . . . . . . . . . . . . . . . . . . ............................................................... 4014
Declaration of Competing Interest . . . . .................................................................................. 4014
Acknowledgments. . . . . . . . . . . . . ...................................................................................... 4014
References . . . . ..................................................................................................... 4014
1. Introduction
Adaptive resistance mechanisms are highly dependent on can-
cer subtypes and applied treatments. Therefore, the resistance
mechanism needs to be defined for each cancer subtype and indi-
vidual treatment plan. Currently, hardly any tools exist to deter-
mine from the beginning whether a patient will respond to a
specific therapy or display resistance. Thus, there is an unmet need
to develop tools to identify drug responses in individual patients
for precision medicine. Recent technological advances have initi-
ated a new era of precision medicine through data-driven assess-
ment of diseases by combining machine learning (ML) and
biomedical science. The use of artificial intelligence such as ML
helps to extract meaningful conclusions by exploiting big data,
thereby improving treatment outcomes. ML is widely used in can-
cer research and is becoming increasingly popular for cancer detec-
tion and treatment. The main goal of precision medicine is to
provide therapies that not only increase the survival chances of
patients but also improve their quality of life by reducing
unwanted side effects. This can be achieved by matching patients
with appropriate therapies or therapeutic combinations.
Some of the early studies on ML and its applications in human
cancer research have been discussed elsewhere [1]. Several recent
overviews in this emerging field have provided valuable insights
into the relevant computational challenges and advancements
[2–8]. These overviews illustrated the importance of the field and
supported the notion that ML is a highly promising approach to
personalized therapy for cancer treatment. In a recent review, a
broad perspective was provided on how ML tools can be incorpo-
rated into clinical practice with a focus on biomarker development
[9]. Another review identified several challenges in omics data
analysis and data integration to obtain robust results in big-data-
assisted precision medicine [10]. Several other reviews dealt pri-
marily with the computational methods and software that are
required to advance data-driven precision oncology [11–13]. Also,
whereas Grothen et. al. discussed artificial intelligence-based
investigations into cancer subtypes and disease prognosis from a
system biology perspective [14], Biswas et. al. reviewed artificial
intelligence applications for pharmacy informatics in a surveillance
and epidemiological context [15]. Another study systematically
explained how deep learning (DL), a subset of ML, has emerged
as a promising technique, highlighting various genomics and phar-
macogenomics data resources [16]. However, the aforementioned
studies did not focus strictly on drug response prediction from
clinical perspectives. In recent years, several surveys and review
articles have presented the potential and challenges of ML adop-
tion in clinical practice and drug response prediction in cancer
treatment [17–23]. Nonetheless, the area of applications of ML in
cancer treatment is so diverse that various issues still need to be
analyzed from a holistic perspective. In this review, we provide a
comprehensive overview of the ML solutions for drug response
prediction relating to the relevant clinical practices. In addition
to discussing the basics of therapy response prediction and related
ML principles, we systematically present the ML and DL
approaches that are promising for monotherapy and combination
therapy in cancer treatment, a focus that makes our article differ-
ent from existing surveys and reviews.
2. Basics of therapy response prediction
Predictive model development involves several steps that com-
bine biological data and ML algorithms. A brief workflow has been
depicted in Fig. 1.
2.1. Pharmacogenomic data resources
High-quality biological data are a prerequisite for a good model.
Large-scale cell line data are publicly available from different plat-
forms and include genomic, transcriptomic, and drug response
data. Pharmacogenomic data for cell lines are available mainly
from the Cancer Cell Line Encyclopedia (CCLE) [24,25], NCI-60
[26], the Genomics of Drug Sensitivity in Cancer (GDSC) [27,28],
gCSI [29], and the Cancer Therapeutics Response Portal (CTRP)
[30,31]. PharmacoDB [32] and CellMinerCDB [33,34] provide
access to the curated data from different studies. These datasets
offer baseline genomic and transcriptomic data for cell lines cover-
ing a wide range of cancers. DrugComb [35] and DrugCombDB [36]
offer manually curated drug combination data from different
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studies. Besides these pharmacogenomic data for cell lines, which
have been widely used to develop ML models, several initiatives
have recently been undertaken to generate pharmacogenomic data
from patient-derived xenografts (PDXs). Compared with cell lines,
PDXs are superior in predicting clinical activities. PDX finder [37],
PRoXE [38], PDMR [39], and EorOPDXs [40] provide comprehensive
data for PDXs. Several other studies also provide high-quality tran-
scriptomic and pharmacogenomic data that are useful for model
development or testing when combined with other datasets [41–
45].
2.2. Data preprocessing
Data preprocessing is an important step in the ML approach.
Large-scale data preprocessing includes data selection, noise filter-
ing, imputation of missing values, feature selection, and
normalization.
Data selection – Data selection remains the most challenging
aspect due to the possible inconsistencies between different data-
sets [46]. Studies comparing the largest public collections of phar-
macological and genomic data for cell lines suggest that each
dataset separately exhibits reasonable predictive power but that
combining datasets can further increase the classification accuracy
[29,47].
Feature selection – Large-scale cell line datasets comprise tran-
scriptomic, mutational, copy number variation (CNV), methylation,
and proteomic data. Although genetic features such as mutations,
CNV, and promotor methylation have been shown to provide
important therapeutic insights, these features seem to be limited
to individual tumors [27]. Therefore, it has been suggested that
transcriptomic features alone hold the most predictive power
and that the addition of genetic features marginally improves per-
formance of an ML model [48–50]. The feature-to-sample ratio
plays an important role in controlling the variances, with a smaller
ratio providing better prediction [51]. However, maintaining a
proper feature-to-sample ratio is challenging for pharmacoge-
nomic data. For example, transcriptomic data can have more than
15,000 features, while the number of samples in any pharmacoge-
nomic study remains between 100 and 1000. Systematically reduc-
ing the number of features (also known as dimensionality
reduction) by incorporating meaningful descriptions improves pre-
diction accuracy by reducing overfitting [52,53]. Several tech-
niques can be used for feature selection, including minimum
redundancy maximum relevance (mRMR), high-correlation filters,
principal component analysis, and backward feature elimination
[54–62].
Data normalization – Because the range of values of raw data
varies widely, a normalization technique (also known as feature
scaling) is applied to change the values of numeric columns in
the dataset to obtain a common scale, so that the associated objec-
tive functions work properly. Different ways exist to perform fea-
ture scaling, including min–max normalization, rank-invariant
set normalization, data standardization, cross-correlation, and
scaling to unit length [63].
3. ML algorithms for drug response prediction
ML algorithms can be grouped into four major classes: super-
vised learning, semi-supervised learning, unsupervised learning,
and reinforcement learning [64,65]. Supervised learning algo-
rithms use a training dataset with known outcomes to build a
hypothetical function with decision variables that can later be used
to predict unknown samples (Fig. 2). On the other hand, unsuper-
vised learning algorithms use unlabeled data to find hidden struc-
tures or patterns; these algorithms are widely used in biological
research for clustering and pattern detection. Semi-supervised
learning algorithms are self-learning and can develop a prediction
model from partially labeled data [66]. A reinforcement learning
algorithm employs a sequential decision problem in which the
algorithm solves a problem and learns from the solution [65].In
this case, the algorithm discovers which actions result in the best
output on a trial-and-error basis. Perhaps supervised learning algo-
rithms are generally used for building classification models, and
these algorithms have also been widely tested for predicting treat-
ment outcomes. Therefore, in this review, we will focus mainly on
supervised learning algorithms.
3.1. Linear regression
Linear regression algorithms are simple and constitute the most
popular ML algorithms, with a wide range of applications. The
standard algorithm, least squares regression, uses the sum of
squared residuals as the cost function to be minimized. Least
squares regression works with a simple dataset; however, with
increasing complexity, the algorithm shows overfitting (low bias
but large variance). To resolve this problem, several algorithms,
Fig. 1. Workflow for ML prediction model development. Pharmacogenomic data from cell lines, patient-derived xenografts (PDXs), and patient materials are ideal for ML
model development. Data from different sources are preprocessed and then divided into training (including cross-validation) and test groups. The training dataset is used to
build and validate the prediction model, while the test dataset is used for testing the model’s accuracy and precision. To develop a prediction model for clinical use, vigorous
preclinical assessment is required that can be performed using cell lines, PDXs, and patient materials that have not been used for model development. Additionally, the
efficacy of predicted drugs must be tested for disease-specific preclinical models. Finally, both the model and predicted drug will undergo a clinical trial.
R. Rafique, S.M. Riazul Islam and J.U. Kazi Computational and Structural Biotechnology Journal 19 (2021) 4003–4017
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such as the ridge model, lasso model, and elastic net, have been
proposed. The cost functions in these models have been modified
to increase the bias and reduce the variance. In a ridge model, a
so-called L2 regularization, which is the squared value of the slope
multiplied by k, has been added to the least squares cost function.
The least absolute shrinkage and selection operator (lasso) regular-
ization (known as L1 regularization) is similar to the ridge regular-
ization, but in this case, the added value is the absolute value of the
slope multiplied by k. The elastic net algorithm adds contributions
from both L1 and L2 regularization; the cost function = min (sum of
the squared residuals + k* squared value of slope + k* absolute
value of slope). The kparameter is a positive number that repre-
sents regularization strength. A larger kvalue specifies stronger
regularization, while a near-zero value removes the regularization
so that all three algorithms become similar to the least squares
model (Fig. 3). By changing the value of k, it is possible to select
meaningful features. Therefore, these methods can be applied to
feature selection as well as to classification and regression prob-
lems [24,28].
3.2. Nonlinear regression
Among the various supervised learning algorithms, the decision
tree is a relatively popular predictive modeling algorithm used to
classify simple data. A decision tree takes data in the root node
and, according to a test rule (representing the branch), keeps grow-
ing until it reaches a decision (representing a leaf node). The inter-
nal nodes represent different attributes (features) [67]. Each
internal node breaks the data into a small subset until it meets a
particular condition. It is a white-box-type algorithm, as each step
can be understood, interpreted, and visualized. Although the deci-
sion tree is useful for simple classification, with a larger dataset
that has many features, it displays poor prediction powers due to
overfitting. To resolve this problem, several advanced decision-
tree-based models have been developed. The random forest algo-
rithm randomly splits (bootstrapping) training data into several
subsets (bagging) and uses each subset to build decision trees
(Fig. 4). The use of multiple random decision trees for prediction
increases the prediction accuracy [68]. Apart from the parallel
use of random multiple decision trees, boosting algorithms, such
as adaptive boosting (AdaBoost) and gradient boosting, use deci-
sion trees sequentially [69,70]. AdaBoost usually uses one-node
decision trees (decision stump), while gradient boosting uses deci-
sion trees of between 8 and 32 terminal nodes. Both adaptive and
gradient boosting algorithms display better prediction perfor-
mance than single decision trees. Furthermore, a more regularized
gradient boosting algorithm, extreme gradient boosting (XGBoost),
outperforms the former gradient boosting algorithms [71].
3.3. Kernel functions
Kernel functions are widely used to transform data to a higher-
dimensional similarity space. Kernel functions can be linear, non-
linear, sigmoid, radial, polynomial, etc. Support vector machines
(SVMs) are among the most popular kernel-based algorithms that
can be used not only for supervised classification and regression
problems but also for unsupervised learning. In a two-
dimensional space, a linear SVM classifier is defined by a straight
line as a decision boundary (maximum margin classifier) with a
soft margin (Fig. 5A). In this case, the soft margins are also straight
lines that represent the minimal distance of any training point to
the decision boundary [72]. With simple one-dimensional data,
the decision boundary can be a point (Fig. 5B); however, for com-
Fig. 2. Schematic representation of different ML algorithms. In a supervised learning model, all data have a known label, while the semi-supervised model can handle
partially labeled data. Both unsupervised and reinforcement learning algorithms can handle unlabeled data.
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Fig. 3. A comparison of different linear regression algorithms. The sklearn.linear_model from SciKit learn was used to generate example plots using a diabetes dataset
provided in SciKit learn. Plots show that by changing the kvalue, regression can be regulated such that with a small kvalue, all linear regression algorithms provide similar
regression. Color code: linear regression – blue, ridge regression – green, lasso – cyan, and elastic net – red. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)
Fig. 4. Schematic representation of random forest algorithm. The three major steps in the random forest algorithm are bootstrapping, bagging, and aggregation. During
bootstrapping, the training dataset is resampled into several small datasets, which are then bagged for the decision tree. The size of the bagged dataset remains the same but
bootstrapped decision trees are different from each other. All decision trees make predictions on test data, and in the aggregation step, all predictions are combined for the
final prediction. For a classification problem, the final prediction is made by major voting, but for a regression problem, the final prediction uses the mean or median value.
Fig. 5. Support vector machine. (A) In a two-dimensional SVM classification system, the maximum margin classifier is a straight line (red line). Support vectors are the
nearest data points from the maximum margin classifier. The distance between support vectors and the maximum margin classifier is denoted as the soft margin. (B) In a
two-group, one-dimensional data space, the decision boundary is a point, as shown by the red line. (C) In a two-group one-dimensional data space where the decision
boundary cannot be drawn by a point, data are transformed by a kernel function to increase the dimension. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)
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plex problems, the data may need to be transformed to a higher
dimension to draw a decision boundary (Fig. 5C).
3.4. Deep learning
DL methods are a type of ML method that can automatically dis-
cover appropriate representations for regression or classification
problems upon being fed with suitable data. The model can learn
complex functions and amplify important aspects to suppress irrel-
evant variations. During training, the algorithm takes the raw input
and processes it through hidden layers using nonlinear activation
functions. The algorithm tries to minimize certain cost functions
by defining values for the weights and biases (Fig. 6A). Usually, gra-
dient descent is used to find the minima. Gradients for all modules
can be determined by using the chain rule for derivatives, a proce-
dure that is known as backpropagation (starting from the output
and moving toward the input) [73]. DL algorithms have been suc-
cessfully employed in various domains, including image classifica-
tion, because of the availability of more data than features. The
development of DL models using genomic or transcriptomic data
is challenging due to the limited number of samples and the pres-
ence of many features. The selection of appropriate features can
reduce the feature-to-sample ratio and, thereby, prevent overfit-
ting. Furthermore, the addition of random dropout layers can help
the model learn important features and reduce overfitting (Fig. 6B).
Convolutional neural networks (CNNs) are useful for feature
learning (Fig. 6C). During the convolution and pooling steps, the
algorithm of a CNN learns important features [73]. CNNs are
widely used for structured data, such as images; however, if the
data are stored in other types of architectures, such as graphs (an
example includes small-molecule drugs with multiple atoms and
chemical bonds), conventional CNNs cannot be used. In this case,
a different type of convolutional neural network, referred to as
the graph convolutional networks (GCNs), could be applied to the
graph data [74]. GCNs have especially been used to extract atomic
features from drug structure (graph) data [75].
4. Monotherapy response prediction
Currently, only a few drug response prediction tools are avail-
able for clinical use. In fact, a couple of linear regression prediction
models are currently being used for certain types of cancers. A
supervised classification model using a 70-gene signature was
developed in 2002 to predict chemotherapy responses in breast
cancer [76]. The method was patented as MammaPrint and is cur-
rently used in the clinic for patients with early-stage breast cancer.
Later, a similar method was developed in which a linear regression
model based on the scores of a 21-gene signature (Oncotype DX)
was used to predict the chemotherapy responses in early-stage,
estrogen-receptor-positive, HER2-negative invasive breast cancer
[77]. Furthermore, a 50-gene signature was employed in multivari-
ate supervised learning (PAM50 or Prosigna, a breast cancer prog-
nostic gene signature assay) to predict treatment responses in
breast cancer [78]. Aside from these simple, cancer-subtype-
specific prediction models that are currently available in the clinic,
most other studies regarding monotherapy predictions are still in
the preclinical phase. Fig. 7 shows an overview of the methods that
have been used to develop monotherapy prediction models in the
past decade (a brief overview is included in Table 1).
Fig. 6. Deep learning (DL). (A) In a deep neural network (DNN) model, each node of the input data layer is fully connected to the hidden layer nodes. The first hidden layer
takes input data, multiplies it by weight, and adds a bias before applying a nonlinear activation function. The second hidden layer takes the first hidden layer as input and so
on until it reaches the output layer. (B) In a dropout layer, some nodes are randomly removed. (C) During the convolution, the dimension of input data is reduced using a
certain kernel size (in this example, 3x3) and the activation function. Then, features are pulled for further reduction. Finally, pulled features are flattened and applied to a
DNN.
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4.1. Classical ML models in monotherapy prediction
Sparse linear regression models have been used to predict drug
sensitivity in initial large-scale pharmacogenomic studies with cell
lines from various cancers [24,28,30]. These studies combined
genomic features with transcriptomic features from cell lines and
correlated them with corresponding drug sensitivity scores. The
ridge regression and elastic net algorithms were predominantly
employed for predictions [24,28,30,50,79]. However, due to the lin-
ear nature of the algorithms and the use of many features, these
models could easily become overfitted.
As discussed above, the performance of prediction algorithms is
largely influenced by biological feature selection [54,55,80,81].
Prediction performance can further be improved by incorporating
information on the similarity between cell lines and drugs [82].
Cell lines with a similar gene expression profile show similar
responses to a specific drug, while drugs with a similar chemical
structure display similar inhibitory effects toward different cell
lines. Therefore, a dual-layer network model that also considers
similarity information outperforms linear models [82]. Likewise,
a method based on a heterogeneous network in which the relation-
ships among drugs, drug targets, and cell lines were explicitly
incorporated was shown to better capture the relationship
between cell lines and drugs [83]. Collectively, a predictive model
with selected features performs better, and the addition of network
features improves the prediction accuracy.
The community-based NCI-DREAM study used a limited num-
ber of samples with a large number of genomic, transcriptomic,
and proteomic features [49]. The NCI-DREAM initiative developed
44 different drug sensitivity prediction models, with the Bayesian
multitask multikernel learning (BM-MKL) models performing rela-
tively better than other models. BM-MKL includes Bayesian infer-
ence, multitask learning, multiview learning (multiple data view),
and kernelized regression [49,84,85]. The standard model, kernel-
ized regression, is a nonlinear classification algorithm similar to
SVMs. Unlike the elastic net, kernelized regression captures the
nonlinear relationship between drug sensitivity and genomic or
transcriptomic features but simplifies the process by using a single
component for the predictions.
Besides using genomic or transcriptomic features to predict
drug sensitivity, the chemical and structural properties (also
known as descriptors) of drugs have been incorporated into the
learning algorithms. Combining drug descriptors with genomic or
transcriptomic data allows for the simultaneous prediction of
Table 1
Studies predicting monotherapy responses.
Year Data Features Algorithm Ref.
2012 GDSC Mutation, CNV, gene expression Elastic net [28]
CCLE Mutation, CNV, gene expression Elastic net [24]
2013 CCLE, GDSC Gene expression (1000 selected genes) Elastic net and other [54]
CTRP Mutation, CNV Elastic net [30]
GDSC Selected genomic features Neural networks and random forests [80]
2014 GDSC, clinical data Gene expression Ridge regression [79]
CCLE, GDSC Mutation, CNV, gene expression Elastic net and ridge regression [50]
GDSC, CCLE, NCI Gene expression (1000 selected genes) Random forest [55]
NCI-DREAM Mutation, CNV, gene expression, proteomic BM-MKL [49]
2015 GDSC, CCLE Gene expression Cell line-drug network model [82]
2016 NCI Mutation, CNV, gene expression, RPLA, miRNA Random forest and support vector machine [81]
GDSC 2 Mutation, CNV, gene expression, methylation Elastic net and random forest [27]
LINCS Gene expression DNN [88]
2018 AML patient and cell line data Gene expression VAE + LASSO (DeepProfile) [99]
GDSC Genomic fingerprints CNN [91]
AML patient and cell line data Gene expression, mutation, CNV, methylation Network-based gene-drug associations [87]
PharmacoDB, CMap Gene expression VAE (Dr.VAE) [59]
CCLE, GDSC Gene expression Recommender systems [94]
2019 GDSC Gene expression DNN [90]
TCGA, CCLE Mutation, gene expression VAE, DL (DeepDR) [60]
GDSC Mutations and CNV CNN ((tCNNS) [105]
GDSC Mutation, CNV, gene expression. DL (MOLI) [92]
GDSC, CCLE Gene expression Autoencoder (DeepDSC) [61]
2020 PDXGEM Gene expression Random forest [106]
GDSC, KEGG, STITCH Gene expression, pathway DL [89]
GDSC, CCLE, CTRP Gene expression, mutation, CNV, methylation VNN [62]
van de Wetering et al. [108], Lee et al. [109] Gene expression, pathway Ridge regression [107]
2021 GDSC Mutations and CNV Graph convolutional network [104]
Fig. 7. ML algorithms used in the last decade to build monotherapy response prediction. Earlier prediction models were likely developed mainly using classical ML
algorithms. Later, the DL algorithms were used mostly to develop the models. The majority of the studies used multi-omics data (mutation, CNV, methylation, and gene
expression) collected from large screening studies such as CCLE, GDSC, CTRP, etc. EN – elastic net, RF – random forest, NN – neural network, RR – ridge regression, BM-MKL –
Bayesian multitask multi-kernel learning, SVM – support vector machine, LASSO - least absolute shrinkage and selection operator, CNN – convolutional neural network, DNN
– deep neural network, AE – autoencoder, VAE – variational autoencoder, MF – matrix factorization, VNN – visual neural network, GCN – graph convolutional network.
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multiple drug responses from a single model, although it is a chal-
lenging task due to the further increase in the total number of fea-
tures [86]. Likewise, in a study with multicancer and multidrug
associations, a disease-specific multi-omics approach to predicting
gene-drug association was adopted in which each gene was
checked for a pathway association [87]. The method is useful for
identifying critical regulatory genes that can be targeted by a drug.
4.2. Deep neural networks in monotherapy prediction
Although DL has long been widely used in several areas of med-
ical science and drug discovery platforms, it has recently been
applied to drug response prediction as well. Initially, feedforward
deep neural networks (DNNs) were applied to develop models
using selected genomic features [80] or transcriptomic data [88].
Later studies incorporated selected gene expression features with
pathway information to build DNN models [89,90]. In any case,
all these DNN models have been shown to outperform classical
ML models.
A CNN was used in the Cancer Drug Response Profile scan
(CDRscan) study, in which convolutions were applied separately
to genomic fingerprints of cell lines and molecular fingerprints of
drugs [91]. After convolution, those two sets of features were
merged and used with the drug response data to develop a DNN
model. Because a CNN learns important features during training
[73], the CDRscan method displays considerably higher robustness
and generalizability. A similar model (MOLI) was developed using
somatic mutations, CNVs, and gene expression data from GDSC
[92]; the model was later validated with PDXs and patient samples.
4.3. Matrix factorization and factorization machines in monotherapy
prediction
Matrix factorization (MF) is a supervised learning method that
has been widely used in popular e-commerce ML recommender
systems [93]. MF takes high-dimensional data, with missing infor-
mation, as input and decomposes it into lower-dimensional matri-
ces with the same numbers of latent factors (Fig. 8A). The learning
algorithms in recommender systems are not general and must be
tailored to each specific model. A modified recommender system
was developed (CaDRReS) in which cell line features were first cal-
culated using gene expression information [94]. The MF method
determined the pharmacogenomic space (the dot product of the
cell line vector and the drug vector), and drug sensitivity was com-
puted using a specific linear algorithm. The model was compared
to other ML algorithms and was found to perform similarly to
the elastic net. Because the model provides a projection of cell lines
and drugs into the pharmacogenomic space, it is easy to explore
relationships between drugs and cell lines [94].
In a recommender system, MF cannot add additional features
and cannot predict a completely new item, as the method is highly
dependent on data from input features. To resolve those issues, in
2010 Rendle introduced a generalized algorithm, the factorization
machine (FM)) [95]. FMs are SVM-like predictors but can handle
data with high sparsity (Fig. 8B). Classical FMs can easily handle
second-order feature combinations but struggle with higher-
order feature combinations. Blondel et al. proposed an updated
algorithm for the easy handling of higher-order feature combina-
tions, referred to as higher-order factorization machines (HOFMs)
[96]. So far, HOFMs have not been used in monotherapy response
prediction; however, they have been employed to predict drug
combinations (as described below).
4.4. Autoencoders in monotherapy prediction
An autoencoder is an unsupervised DL model that can be used
to reduce the dimension of features. An autoencoder learns hidden
(latent) variables from the observed data through the mapping of
higher-dimensional data onto a lower-dimensional latent space.
An autoencoder consists of two different types of layers: encoding
layers and decoding layers, with encoding layers projecting higher-
dimensional input data onto lower dimensions and decoding layers
reconstructing the lower-dimensional data back to the higher-
dimensional data similar to input (Fig. 9A). The loss function is
the least squares difference between the input and output vectors.
In this case, if the decoding weights correspond to the encoding
weights, the output will be the same as the input (deterministic
encoding). In general, an autoencoder uses nonlinear activation
functions for data compression and can discover nonlinear
explanatory features; therefore, it can be used to reduce gene
expression features and uncover a biologically relevant latent
space [61,97].
Besides the traditional autoencoder, the variational autoen-
coder (VAE) replaces the deterministic bottleneck layer with
stochastic sampling (mean and standard deviation) vectors
(Fig. 9B). The model includes regularization losses by adding a
Kullback-Leibler (KL) divergence term. This reparameterization
allows for backpropagation optimization and for learning the prob-
ability distribution of each latent variable instead of directly learn-
ing the latent variables [98].
The DL model to predict drug response (DeepDR) combined
mutational data with gene expression data to develop a monother-
apy prediction model, implementing an autoencoder for both
mutational and gene expression data [60]. In this model, the
autoencoder was first applied to the TCGA data to transform the
mutational and gene expression features into a lower-
dimensional representation. The encoded representations of the
TCGA data were linked to a feedforward neural network trained
on CCLE data for monotherapy prediction. The use of autoencoding
Fig. 8. Matrix factorization and factorization machine. (A) In MF, a matrix is decomposed into two lower-dimensional matrices with the same latent factor. The dot product of
lower-dimensional matrices is used to reconstitute the new matrix to calculate the loss function. (B) An FM transforms sample and features data to the binary representation
and can incorporate additional features.
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increased the sample number in the prediction model and, there-
fore, displayed better prediction performance. Besides an autoen-
coder, a VAE was used to reduce the higher-dimensional acute
myeloid leukemia (AML) patient gene expression data to an 8-
dimensional representation, and the VAE was then used to build
a linear regression model (lasso) for drug response prediction
[99]. Later, a drug response VAE (Dr.VAE) was developed using
drug-induced gene expression perturbation [59]. This study used
a semi-supervised VAE to predict monotherapy responses using
cell line data, and the model was shown to perform better than
several linear or nonlinear algorithms. The use of drug-induced
gene expression perturbation seems to be useful in determining
pathways that regulate drug response and therapy resistance
[100]. Nevertheless, anomaly detection with density estimation
can improve the prediction accuracy through false positive detec-
tion, but this still needs to be implemented [101].
4.5. Graph convolutional networks in monotherapy prediction
Therapy response prediction using multiple drugs requires the
incorporation of chemical information about the drugs. This can
be done in several ways. The 2D molecular fingerprint (also known
as the Morgan fingerprint or circular fingerprint) is commonly
measured by the extended-connectivity fingerprint (ECFP) algo-
rithm [102]. This algorithm determines partial structures and con-
verts them into a binary representation. Similarly, the 3D
fingerprint descriptor collects 3D information, including electro-
statics and molecular shape. The simplified molecular input line
entry specification (SMILES) representation was developed by Wei-
ninger and provides a linear notation method [103]. SMILES can be
used directly by a CNN. Molecular graphs are another type of flex-
ible representation of small-molecule drugs. The GraphDRP study
used a molecular graph representation in a GCN to extract molec-
ular features from drugs [104]. At the same time, a CNN was used
to extract genomic features from cell lines. Then, the features from
the GCN and CNN were combined and fed into the fully connected
feedforward neural network for drug sensitivity prediction. The
GCN model was compared to a recently developed CNN model
using the SMILES format to describe the drugs and was found to
perform better, suggesting that the use of graph data for drugs
improves predictive performance [105].
4.6. Visible neural networks in monotherapy prediction
Model interpretation is an important research area in ML that
seeks to explain the model’s internal rationality of a prediction.
Biological ML models that were developed with prior knowledge
of network or structural data can be explained relatively easily. A
so-called visible neural network (VNN) incorporates genomic or
transcriptomic data considering the cellular architecture and sig-
naling pathways [62]. Chemical information about drugs was sep-
arately processed and then combined with the embedding
genotype data to develop the final prediction model (DrugCell).
The DrugCell method was compared to the elastic net and other
DNN models and found to have a similar or better predictive
performance.
4.7. PDXs and organoids in monotherapy prediction
Although most studies used cell line data to develop ML models,
recently the PDXGEM study applied PDXs to develop an ML model
[106]. In this study, drug activity was calculated as a percentage of
tumor volume changes. Baseline gene expression profiling data
were used to develop the model. Another recent study used data
from 3D organoid culture models and applied protein–protein
interaction networks [107]. The model was trained with pharma-
cogenomic data from two previous studies using ridge regression
[108,109]. This study developed a clinically relevant prediction
model that was also useful in identifying predictive biomarkers
[107]. Collectively, the use of PDXs and organoids in model devel-
opment increases the probability of successful clinical applications.
5. Drug synergy prediction
The use of monotherapy in cancer treatment is relatively rare,
and most cancer patients are treated with a combination of several
drugs. Cancer cells can easily develop resistance to monotherapy,
while the development of resistance to several drugs can be diffi-
cult or take longer. Therefore, combinatorial therapies are pre-
ferred over monotherapy in clinics for cancer treatment. A
combination of multiple drugs can have three different effects:
additive, antagonistic, and synergistic. The additive effect can be
considered a neutral effect, while the antagonistic effect is nega-
tive. The synergistic effect is preferable. Thus, predicting drug syn-
ergy will be highly beneficial for selecting effective combinations
for cancer treatment.
Drug synergy is usually calculated by a cell viability matrix, in
which a wide range of single and combinatorial drug effects are
noted. The Institute for Molecular Medicine Finland (FIMM) devel-
oped an experimental-computational pipeline to measure and
visualize synergy from drug combinations [110]. It allows for the
simultaneous measurement of several synergy scores, such as Bliss
independence [111], Loewe additivity [112], highest single agent
(HSA) [113], and zero interaction potency (ZIP) [114]. Later, the
study was extended to the prediction of drug combinations
[115]. Combenefit is yet another program for calculating synergy
scores, in particular Loewe additivity [116].
Several attempts have been made to identify drug synergy using
cell lines from different cancers [117–123]. These studies provided
an initial framework for developing ML algorithms for predicting
drug synergy. A list of available in silico drug synergy prediction
models is given in Table 2.
Fig. 9. Autoencoder and variational autoencoder. (A) The autoencoder determines latent variables by reducing the dimensions during encoding. Then it decodes the data into
a similar form using the latent variables. (B) VAE uses a similar process unless the latent variables are replaced by the mean and standard deviation.
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5.1. Drug synergy prediction using conventional ML methods
In silico methods integrating molecular data with pharmacolog-
ical data could potentially identify drug combinations with some
limitations [124]. A heterogeneous network-assisted inference
(HNAI) framework was developed using drug-drug interaction
pairs connecting approved drugs, phenotypic similarity, therapeu-
tic similarity, chemical structure similarity, and genomic similarity
using naive Bayes, decision tree, k-nearest neighbor (KNN), logistic
regression, and SVM algorithms [125]. Then, the DDIGIP method, in
which the Gaussian interaction profile (GIP) kernel and the regu-
larized least squares (RLS) classifier were implemented, was based
on drug-drug interactions (DDIs) [126]. DDIGIP used the similarity
of drug features extracted from drug substructures, targets, trans-
porters, enzymes, pathways, indications, side effects, offside
effects, and drug-drug interaction data. Collectively, these methods
give valuable insights into drug-drug interactions but cannot pro-
vide information about whether certain drug combinations will be
effective for a specific patient. Gene expression data were used at a
limited scale to predict the effect of drug combinations by the Petri
net model [127], but the model requires gene expression profiles
for every drug pair, which limits its practical applications.
In a DREAM challenge, the human diffuse large B-cell lym-
phoma (DLBCL) cell line OCI-LY3 was treated with 91 compound
pairs of 14 drugs. The drug-induced genomic residual effect
model—which combined similarity and dissimilarity in compound
activity incorporating drug-induced gene perturbation, dose–re-
sponse, and pathway information—was reported to outperform
30 other models [128,129]. Although the accuracy of the predictive
models was not optimal for practical applications, this study raised
the probability of building computational predictive models for
drug synergy prediction. The gene expression perturbation data
generated in this project are valuable for other studies and can
be used to train random forest models with the biological and
chemical properties of drugs, such as physicochemical properties,
target network distances, and targeted pathways [130]. Similarly,
Cuvitoglu et al. extracted the drug perturbation set of genes for
each drug from the transcriptome profile of Cmap data [131] and
calculated six different features: the distance between two drugs
(M1), the mutual information about biological processes (M2),
the gene ontology similarity (M3), the overlap of drug perturbation
sets (M4), the betweenness centrality of the drug combination net-
work (M5), and the degree of the drug combination network (M6)
[132]. Three models were developed using a naive Bayes classifier,
an SVM, and a random forest algorithm. Different features were
tested, and models combining the M5 and M6 features performed
the best. In addition, the CellBox method used perturbation data of
the melanoma SK-Mel-133 cell line treated with 12 different drugs
[133,134]. Using nonlinear ordinary differential equations (ODEs),
CellBox provided an interpretable ML system that can be used to
predict drug combinations in a dynamic system. This study pro-
vided mechanistic insights for designing a combination therapy
with an understandable predictive model. Taken together, these
studies suggest that drug perturbation data provide important
information about the regulation of biological features that can
be used to develop efficient ML models [100].
Models integrating the signaling network or pathway map have
been used to detect drug combinations with limited general appli-
cations [135–137]. Similarly, synergy prediction models developed
with naive Bayes classifiers [138] and random forest algorithms
[139,140] had limited use for specific cell models. Collectively, syn-
ergy prediction models developed using classical ML algorithms
displayed acceptable predictive performance with specific datasets
but largely lacked generalizability.
5.2. Drug synergy prediction using DL
DL has been employed in the prediction of drug synergy. Using
the NCI-ALMANAC database [141], it has been demonstrated that
the use of gene expression, microRNA, and proteome data, along
with drug descriptors, provides the highest prediction capability
with feedforward neural networks [142]. This model used two sub-
models to separately process drug descriptors and gene expression,
microRNA, and proteome data. The submodels were fully con-
nected neural networks that helped reduce the dimensionality of
the data before they were fed into the final model. This study pro-
vided important insight into the use of DL in feature selection and
model development.
The DeepSynergy study [143] used a previously published drug
synergy dataset [122] to build a DL model and compared it with
several classical ML methods, such as gradient boosting, random
forest algorithms, SVMs, and elastic nets. This feedforward DL
model, which used gene expression data with the chemical fea-
tures of both drugs to predict Loewe additivity, achieved consider-
able accuracy. The use of DL allowed the model to perform better
than other ML algorithms, but it should also be tested with
unknown samples.
Recently, transformer boosted DL (TransSynergy) was devel-
oped, in which three components were used: input dimension
reduction, a self-attention transformer, and a fully connected out-
put layer [144]. The input vector contained selected features from
two drugs (drug-target interaction profile) and the cell line (gene
Table 2
Studies predicting drug synergy.
Year Study name Data Algorithm Ref
2015 RACS DCDB [151], KEGG, NCI-DREAM Semi-supervised learning [118]
2017 Li et al. DREAM [128] Random forest [130]
Gayvert et al. Held et al. [120] Random forest [140]
SynGeNet LINCS L1000, Held et al. [120] Network-based [136]
2018 Xia et al. NCI-ALMANAC [141] DL [142]
Deep Synergy O’Neil 2016 [122] DL [143]
Deep belief DREAM [128] Restricted Boltzmann machine [150]
2019 SynGeNet LINCS L1000 Network based [137]
DREAM CNV, mutation, methylation, and gene expression Multiple [117]
DDIGIP DrugBank, SIDER, OFFSIDES Regularized Least Squares [126]
Cuvitoglu et al. DCDB [151], Cmap [131] Naive Bayes, Support Vector Machines, and Random Forest [132]
Malyutina et al. O’Neil 2016 [122] Elastic net, random forest, support vector machine [115]
2020 Deep graph O’Neil 2016 [122] graph convolutional network [147]
comboFM NCI-ALMANAC [141] Higher-order factorization machines [145]
2021 CellBox Perturbation data [134] ODE [133]
AuDNNsynergy O’Neil 2016 [122] Autoencoder [146]
TranSynergy O’Neil 2016 [122] Transformer boosted DL [144]
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4012
expression). A fourth dimension was added if both gene expression
and gene dependency were used. The use of cell-line-gene depen-
dency, gene-gene interaction, and drug-target interaction provided
TransSynergy with a considerably higher predictive performance
and allowed the cellular effect of drug actions to be explained.
These methods provided a significant improvement over tradi-
tional ML mechanisms due to appropriate feature learning. How-
ever, all those models used cell line synergy data [122], which
might limit their application in preclinical and/or clinical trial
settings.
5.3. Synergy prediction with a higher-order factorization machine
An HOFM model [96] was used in comboFM to capture fifth-
order feature combinations using data from two drugs, cell lines,
and dose–response matrices [145]. The model integrated chemical
descriptors of drugs and gene expression data of cell lines as addi-
tional features. comboFM was trained with a part of the NCI-
ALMANAC data, while the other part of the data was used for pre-
dictive performance testing. The fifth-order comboFM was found
to perform significantly better than second- and first-order predic-
tors, suggesting that the use of higher-order feature combinations
can improve predictive performance.
5.4. Synergy prediction using an autoencoder
An autoencoder has also been employed to predict drug synergy
[146]. AuDNNsynergy used multi-omics data from CCLE and TCGA
databases combined with previously published drug synergy data
[122]. In this study, three independent autoencoders were used
to reduce the dimensions of TCGA gene expression, mutation,
and copy number data. The reduced dimensions were then com-
bined with drug combination data to develop the model. The
model was compared with the recently developed DeepSynergy
model and was shown to perform better [143], suggesting that fea-
ture reduction using an autoencoder and the use of multi-omics
data influence predictive performance.
5.5. Synergy prediction with a graph convolutional network
A graph convolutional network (GCN) model was described
(DeepGraph) in which a drug-drug synergy network, a drug-
target interaction network, and a protein–protein interaction net-
work were used to build a cell-line-specific model [147]. In the
DeepGraph study, a cell-line-specific multirelational network
graph was generated and fed into the GCN encoder. A four-layer
neural network with a relu activation function was used for encod-
ing, and a sigmoid activation function was used for the embedding
output vector. The matrix decoder was used to decode the embed-
ding vector, which predicts the synergy score [74]. The prediction
performance of DeepGraph was comparable to that of DeepSyn-
ergy. Because the DeepGraph method used a cell-line-specific
drug-protein network and protein–protein interaction network
and because only limited data for drug-protein interactions were
available, the method’s performance might be biased.
5.6. Restricted Boltzmann machine for predicting drug synergy
The restricted Boltzmann machine (RBM) is a generative proba-
bilistic model that has been widely used for handling higher-
dimensional data [148]. The RBM is similar in function to an
autoencoder and can be used to extract meaningful features from
higher-dimensional data. Furthermore, multiple RBMs can be
stacked to form a deep belief network, which allows unsupervised
and supervised data to be combined. RBMs have been used to iden-
tify gene expression biomarkers that can help predict clinical out-
comes [149]. Chen et al. used RBMs to develop a deep belief
network [150] from the DREAM consortium’s drug target informa-
tion and baseline gene expression data [128]. Although the model
was compared with existing DREAM consortium models and was
shown to outperform these models, the leave-one-out approach
that was adopted in this study was not comparable to the original
DREAM consortium models, which were compared with external
data.
6. Limitations in the development of clinically relevant
predictive models
Currently, most ML models have been developed using cell line
data. Cell line data are robust, relatively easy to generate, and use-
ful for hypothesis generation. However, cell line data must be com-
plemented with more disease-relevant patient data. A large-scale
pharmacogenomic study using patient data is currently technically
difficult because it requires a lot of primary patient materials. This
can potentially be overcome by using PDXs. The recent develop-
ment of PDX repositories will support large-scale clinically rele-
vant studies in the near future [37–40].
Most tumors grow in a multicellular environment in which the
surrounding cells create a favorable microenvironment for tumor
growth. Prediction models based on cell line data do not capture
the microenvironment’s contributions and might therefore never
reach the level of accuracy that is necessary in the clinic. Cultured
tumor organoids can likely mimic the microenvironment of a
patient’s tumor [107]. However, currently, only limited pharma-
cogenomic data from tumor organoids are available.
Several recent models used multi-omics data to build predictive
models [62,87,92]. Although the use of multi-omics data can
improve the prediction performance and can be very useful for
research purposes, it limits the practical use of the models in the
clinic. For prediction purposes, it would be costly and time-
consuming to determine mutations, CNVs, promotor methylation,
protein expression, gene expression, etc. for each patient sepa-
rately. Gene expression data can potentially reflect most cellular
processes because mutations, CNVs, and promotor methylation
might ultimately determine gene expression changes.
Most gene expression data currently available involve the base-
line expression of genes and do not reflect drug-induced perturba-
tions [24,28,30,80]. A few studies provided a limited number of
drug-induced perturbation data, which were found to be very use-
ful for feature selection [59,134]. Thus, large-scale drug-induced
perturbation studies will help to develop better predictive models.
Nevertheless, drug synergy prediction is an important concept
that will have numerous uses in the clinic. At the same time, a
combination of several drugs can have severe adverse effects. Thus,
a comprehensive method is needed that will not only determine
drug synergy but also incorporate the adverse effect of drug com-
binations. Knowledge of safe and unsafe combinations of drugs
was used to build a linear regression prediction model [152–
154]. However, the model did not incorporate any biological data
to elucidate patient-specific side effects.
Several studies have highlighted implementation challenges
encountered in precision medicine solutions [155,156]. These chal-
lenges include data preprocessing, unstructured clinical text pro-
cessing, medical data processing and storage, and environmental
data collections. Apart from these challenges, the major challenge
might be the redesigning of clinical decision support systems so
that they can incorporate molecular, omics, and environmental
aspects of precision medicine. A comprehensive support system
is desirable to facilitate the curation of data from different sources
and multiple scales and to promote the interaction between bioin-
R. Rafique, S.M. Riazul Islam and J.U. Kazi Computational and Structural Biotechnology Journal 19 (2021) 4003–4017
4013
formatics and clinical informatics [155]. Building such a system
requires solving many integration and standardization issues.
As pointed out by many studies, model explainability, high-
quality training data, and collaborations between medical experts
and computational experts are some of the key factors affecting
the success of ML solutions for drug response prediction in cancer
treatment [9,157]. Although much omics information is available
and many theoretical frameworks exist, hands-on ML tools tar-
geted at physicians and medical professionals are scarce. In that
regard, various cloud-based cancer prediction tools, such as OASIS-
PRO [158], can be introduced to make ML solutions suitable for
massive clinical practice. The study gave an overview of general-
purpose multi-omics tools that can be useful for gene identification
and cancer subtyping [159].
Clinical trials are essential for clinical research in general and
cancer treatment in particular. The three-phase trial approach is
considered standard practice but is designed primarily for gradu-
ally improving treatments. Our ability to understand and treat can-
cer has, however, evolved over time [21]. Because of the immense
role of ML in both clinical trials and clinical practice, the inclusion
of ML in regulatory frameworks is unavoidable.
7. Conclusion
The development of predictive models for monotherapy and
combinatorial therapies is important but highly challenging. The
recent advancement in ML algorithms holds promise for the devel-
opment of clinically relevant predictive models. Furthermore, more
pharmacogenomic data from disease-relevant organoids and PDXs
are becoming available, allowing clinical biases to be overcome.
Massive computational power is within easy reach for handling a
large amount of data that is exponentially increasing. In the near
future, the current lack of clinically relevant pharmacogenomic
data might also be overcome. Therefore, although current predic-
tive models are far from being ready for clinical use, they show
us a clear path toward precision medicine.
CRediT authorship contribution statement
Raihan Rafique: Writing - original draft, Writing - review &
editing. S.M. Riazul Islam: Writing - original draft, Writing -
review & editing. Julhash U. Kazi: Conceptualization, Writing -
original draft, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared
to influence the work reported in this paper.
Acknowledgments
This research was supported by the Crafoord Foundation (JUK),
the Swedish Cancer Society (JUK), and the Swedish Childhood Can-
cer Foundation (JUK). Open Access funding is provided by Lund
University.
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... However, monotherapy can promote the development of drug resistances. Hence, there also exist methods that predict the sensitivity for drug combinations using cancer cell line panels 1,2,19,20 . A general issue with drug sensitivity data is that the high specificity of (targeted) anti-cancer drugs leads to an underrepresentation of sensitive samples. ...
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... In their study, they stated that machine learning applications have potential opportunities for clinical studies in the field of psychotherapy. In another study, Rafique et al. [13] examined the effect of machine learning on cancer treatment. ...
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... For the RFR model, n_estimators, max_depth, min_samples_split were all optimally adjusted using the GridSearchCV package. Although the DT is a popular and effective modeling algorithm mainly for predicting simple data [58], it can be more easily overfitted compared with the RFR, and provides poor prediction results [59]. The Lasso method obtains a relatively refined model by constructing a penalty function and forcing the sum of the absolute values of some regression coefficients to be within a fixed value and setting some of the other regression coefficients to be zero [60]. ...
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Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases such as cancer. Artificial intelligence has empowered research in the healthcare sector. The availability of open-source healthcare datasets has motivated the researchers to develop applications which helps in early diagnosis and prognosis of diseases. Further, Next-generation sequencing has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective approach with higher accuracy. The advent of microRNAs also known as small noncoding genes has begun the paradigm shift in oncological research. We are now able to profile expression profiles of RNAs using RNA-seq data. microRNA profiling has helped in uncovering their relationship in various genetic and biological processes. Here in this paper, we present a review of the machine learning perspective in cancer research. The best way to develop effective cancer treatment/drugs is to better understand the intricacies and complexities involved in the cancer microenvironment. Although there has been a plethora of methods and techniques proposed in the literature, still the deadliness of cancer can't be reduced. In such a situation Artificial intelligence (AI) or machine learning is providing a reliable, fast, and efficient way to deal with such stringent diseases.
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PURPOSE The implementation and utilization of electronic health records is generating a large volume and variety of data, which are difficult to process using traditional techniques. However, these data could help answer important questions in cancer surveillance and epidemiology research. Artificial intelligence (AI) data processing methods are capable of evaluating large volumes of data, yet current literature on their use in this context of pharmacy informatics is not well characterized. METHODS A systematic literature review was conducted to evaluate relevant publications within four domains (cancer, pharmacy, AI methods, population science) across PubMed, EMBASE, Scopus, and the Cochrane Library and included all publications indexed between July 17, 2008, and December 31, 2018. The search returned 3,271 publications, which were evaluated for inclusion. RESULTS There were 36 studies that met criteria for full-text abstraction. Of those, only 45% specifically identified the pharmacy data source, and 55% specified drug agents or drug classes. Multiple AI methods were used; 25% used machine learning (ML), 67% used natural language processing (NLP), and 8% combined ML and NLP. CONCLUSION This review demonstrates that the application of AI data methods for pharmacy informatics and cancer epidemiology research is expanding. However, the data sources and representations are often missing, challenging study replicability. In addition, there is no consistent format for reporting results, and one of the preferred metrics, F-score, is often missing. There is a resultant need for greater transparency of original data sources and performance of AI methods with pharmacy data to improve the translation of these results into meaningful outcomes.