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Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

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Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.
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International Journal of
Molecular Sciences
Review
Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing
Data to Foster Precision Oncology
Marco Del Giudice 1, 2, , Serena Peirone 1, 3, , Sarah Perrone 1,4, Francesca Priante 1,4, Fabiola Varese 1,5,
Elisa Tirtei 6, Franca Fagioli 6,7 and Matteo Cereda 1,2 ,*


Citation: Del Giudice, M.; Peirone, S.;
Perrone, S.; Priante, F.; Varese, F.;
Tirtei, E.; Fagioli, F.; Cereda, M.
Artificial Intelligence in Bulk and
Single-Cell RNA-Sequencing Data to
Foster Precision Oncology. Int. J. Mol.
Sci. 2021,22, 4563. https://doi.org/
10.3390/ijms22094563
Academic Editor: Jung Hun Oh
Received: 20 March 2021
Accepted: 23 April 2021
Published: 27 April 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Cancer Genomics and Bioinformatics Unit, IIGM—Italian Institute for Genomic Medicine, c/o IRCCS,
Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy; delgiudice.borsisti@iigm.it (M.D.G.);
serena.peirone@edu.unito.it (S.P.); sarah.perrone@edu.unito.it (S.P.); priante.borsisti@iigm.it (F.P.);
varese.borsisti@iigm.it (F.V.)
2Candiolo Cancer Institute, FPO—IRCCS, Str. Prov.le 142, km 3.95, 10060 Candiolo, TO, Italy
3Department of Physics and INFN, Universitàdegli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
4Department of Physics, Universitàdegli Studi di Torino, via P.Giuria 1, 10125 Turin, Italy
5
Department of Life Science and System Biology, Universitàdegli Studi di Torino, via Accademia Albertina 13,
10123 Turin, Italy
6
Paediatric Onco-Haematology Division, Regina Margherita Children’s Hospital, City of Health and Science of
Turin, 10126 Turin, Italy; elisa.tirtei@gmail.com (E.T.); franca.fagioli@unito.it (F.F.)
7Department of Public Health and Paediatric Sciences, University of Torino, Turin, Italy
*Correspondence: matteo.cereda@iigm.it; Tel.: +39-011-993-3969
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint
First Authors.
Abstract:
Artificial intelligence, or the discipline of developing computational algorithms able to
perform tasks that requires human intelligence, offers the opportunity to improve our idea and
delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for
the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to dis-
entangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement
of patient management. We outline the contributions of learning algorithms to the needs of cancer
genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.
Keywords: artificial intelligence; RNA sequencing; cancer heterogeneity
1. Introduction
Artificial intelligence (AI) is becoming a fundamental asset for healthcare and life
science research. Despite being in its infancy, research activities employing AI are chang-
ing our understanding and vision of science. The European Commission has recently
estimated that 13% of global venture capital investments (i.e., ~5 billion of Euros) are
for start-ups dedicated to AI application in medicine [
1
]. This commitment reflects the
interest in the potential of AI to improve healthcare. Precision medicine is a new approach
to health. In the last decade, the generation of Big Data through genome sequencing
(i.e., genomic Big Data), the collection of clinical data, and the growth of bioinformatics
has made it possible to identify the genetic causes responsible for onset and progression of
diseases and to support the clinical management of patients. Despite the high expectations,
personalized therapeutic treatments still remain limited. A breakdown is the lack of AI
infrastructure and models capable of supporting the constant generation of genomic Big
Data [
2
]. Consequently, the challenge remains how to interpret the variety of information
contained in these data [3].
The need for AI models is even more evident in complex diseases such as cancer.
The heterogeneity that characterizes Big Data is amplified in cancer, where diversity not
only manifests itself across individuals (i.e., inter-tumor) but also within each tumor
Int. J. Mol. Sci. 2021,22, 4563. https://doi.org/10.3390/ijms22094563 https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2021,22, 4563 2 of 18
(i.e., intra-tumor) [
4
]. So far, cancer sequencing projects have made available genomic
profiles for thousands of biological samples, corresponding to petabytes of genetic in-
formation [
5
]. With the introduction of single-cell technologies, the complexity of ge-
nomic information has grown rapidly. This heterogeneity represents the major hurdle
to achieve effective precision oncology. Therefore, AI is the pivotal tool to exploit the
information available in genomic Big Data and ultimately “deliver” a medicine of preci-
sion.
The COVID-19
pandemic has opened up new possibilities for AI development. The
pandemic has increased the use of AI in biomedical research: from remotely monitoring
patients, to predicting the spread of the SARS-CoV-2 coronavirus or in developing new
drugs [
6
,
7
]. The pandemic has also brought about new clinical practices, primarily the use
of mRNA vaccines. This technological leap forward gives the possibility of accelerating the
delivery of similar therapies to cancer [8].
Transcriptomics generally refers to the high-throughput profiling of all RNA species
produced by cells. Among genomic Big Data, transcriptomics has seen an explosive growth
in recent years [
9
]. RNA sequencing (RNA-seq) profiles dynamic biological processes that
are active in a population of cells or in single cells. Assessing the complexity of these
profiles could inform the discovery of new biomarkers and therapeutic targets. Since
RNA-seq screenings are becoming part of precision medicine trials [
10
,
11
], AI mining of
these data is thus required to determine novel clinical targets.
In this paper, we provide an overview of AI approaches applied to high-volume bulk
and single-cell RNA-seq in cancer genomics and precision oncology. We do not intend to
provide a comprehensive characterization of all published AI methods and their technical
details. By contrast, we illustrate the major AI solutions to disentangle the heterogeneity of
cancer transcriptomes for an effective improvement of patient management. We explain
distinct strategies to face the “heterogeneity challenge”. We then outline some of the major
contributions of applying AI to the needs of cancer genomics, from identifying rare cancer
subtypes to personalizing treatment for individuals.
2. AI in the Era of Transcriptomic Big Data
From the first drafts of the human genome [
12
], 20 years ago, the number of scien-
tific works employing sequencing data has exponentially increased (Figure 1). RNA-seq
has become a widespread tool to profile cancer transcriptome at both population and
single-cell level.
Int.J.Mol.Sci.2021,22,xFORPEERREVIEW2of18
manifestsitselfacrossindividuals(i.e.,intertumor)butalsowithineachtumor(i.e.,intra
tumor)[4].Sofar,cancersequencingprojectshavemadeavailablegenomicprofilesfor
thousandsofbiologicalsamples,correspondingtopetabytesofgeneticinformation[5].
Withtheintroductionofsinglecelltechnologies,thecomplexityofgenomicinformation
hasgrownrapidly.Thisheterogeneityrepresentsthemajorhurdletoachieveeffective
precisiononcology.Therefore,AIisthepivotaltooltoexploittheinformationavailablein
genomicBigDataandultimately“deliver”amedicineofprecision.TheCOVID19pan
demichasopenedupnewpossibilitiesforAIdevelopment.Thepandemichasincreased
theuseofAIinbiomedicalresearch:fromremotelymonitoringpatients,topredictingthe
spreadoftheSARSCoV2coronavirusorindevelopingnewdrugs[6,7].Thepandemic
hasalsobroughtaboutnewclinicalpractices,primarilytheuseofmRNAvaccines.This
technologicalleapforwardgivesthepossibilityofacceleratingthedeliveryofsimilarther
apiestocancer[8].
TranscriptomicsgenerallyreferstothehighthroughputprofilingofallRNAspecies
producedbycells.AmonggenomicBigData,transcriptomicshasseenanexplosive
growthinrecentyears[9].RNAsequencing(RNAseq)profilesdynamicbiologicalpro
cessesthatareactiveinapopulationofcellsorinsinglecells.Assessingthecomplexityof
theseprofilescouldinformthediscoveryofnewbiomarkersandtherapeutictargets.Since
RNAseqscreeningsarebecomingpartofprecisionmedicinetrials[10,11],AIminingof
thesedataisthusrequiredtodeterminenovelclinicaltargets.
Inthispaper,weprovideanoverviewofAIapproachesappliedtohighvolumebulk
andsinglecellRNAseqincancergenomicsandprecisiononcology.Wedonotintendto
provideacomprehensivecharacterizationofallpublishedAImethodsandtheirtechnical
details.Bycontrast,weillustratethemajorAIsolutionstodisentangletheheterogeneity
ofcancertranscriptomesforaneffectiveimprovementofpatientmanagement.Weexplain
distinctstrategiestofacethe“heterogeneitychallenge”.Wethenoutlinesomeofthemajor
contributionsofapplyingAItotheneedsofcancergenomics,fromidentifyingrarecancer
subtypestopersonalizingtreatmentforindividuals.
2.AIintheEraofTranscriptomicBigData
Fromthefirstdraftsofthehumangenome[12],20yearsago,thenumberofscientific
worksemployingsequencingdatahasexponentiallyincreased(Figure1).RNAseqhas
becomeawidespreadtooltoprofilecancertranscriptomeatbothpopulationandsingle
celllevel.
Figure1.ThegraphshowsthenumberofPubMedpublicationsperyearscontainingthereported
keywords.
Figure 1.
The graph shows the number of PubMed publications per years containing the
reported keywords.
Int. J. Mol. Sci. 2021,22, 4563 3 of 18
So far, genomic screenings have made available more than 106,585 RNA-seq samples
(Table 1) and this number is constantly increasing.
Table 1.
The table reports the number of publicly available bulk and single-cell RNA-seq experiments.
Data stored in reported repository are frozen at 15 March 2021.
Repository URL Bulk Single-Cell
GDC portal.gdc.cancer.gov 27,894 18
ENCODE www.encodeproject.org 2323 7
GEO www.ncbi.nlm.nih.gov/geo 30,510 2346
SRA www.ncbi.nlm.nih.gov/sra 1874 6428
St. Jude www.stjude.cloud 3215 -
ICGC dcc.icgc.org 12,840 -
GTEx www.gtexportal.org/home 17,382 -
DepMap depmap.org/portal 1376 -
Human Cell Atlas data.humancellatlas.org - 289
Single Cell Portal singlecell.broadinstitute.org - 83
The availability of these data seizes the opportunity to boost the development of
novel diagnostic tools and targeted treatments. Indeed, the implementation of AI models
has increased in the last 10 years, as machine-learning [
13
] (ML) and, recently, as deep-
learning [
14
] methods (DL, Figure 1). These learning methods effectively leverage the
variability of Big Data to achieve consistent predictions without the need of modeling the
system of interest [
15
]. In the flavor of supervised, semi-supervised and unsupervised, AI
algorithms can be employed to capture dependencies, make predictions and recognize
patterns in heterogeneous datasets [
13
]. AI approaches are commonly used to solve
regression, classification, dimensionality reduction and clustering tasks. Being part of AI,
ML and DL aim at performing tasks that normally require human intelligence. ML and
DL accomplish similar tasks with distinct mathematical approaches. While ML algorithms
still need human guidance to improve their predictions, DL methods can autonomously
determine the accuracy of a prediction. Overall, DL is part of ML where algorithms are
generally based on artificial neural networks (NNs), the closest representation of the human
brain [16] (Figure 2).
In cancer transcriptomics, ML and DL models have been applied to classify different
cancer subtypes and cell populations [
17
20
], characterize tumor immune microenviron-
ment [
21
25
], discover new prognostic biomarkers [
26
28
], assess and predict disease recur-
rence and patient survival [
29
32
], identify new putative actionable vulnerabilities [
33
,
34
],
and predict tumor antigen immunogenicity [35] (Figure 2).
Disentangling Big Data heterogeneity is the major challenge that researchers have
to face to gain novel scientific insights. When learning from real transcriptomic data, the
heterogeneity increases due to the variable expression of genes across samples driven by
genetic, environmental, demographic, and technical factors [
36
]. In cancer, the complexity
is additionally hampered by the intra- and inter-tumoral heterogeneity of samples [
37
].
Nevertheless, the constant sequencing of transcriptomes, and thus the increasing volume
of data, represents on its own a solid ground for the application of learning approaches to
disentangle the noise from the true biological signal.
Int. J. Mol. Sci. 2021,22, 4563 4 of 18
Int.J.Mol.Sci.2021,22,xFORPEERREVIEW3of18
Sofar,genomicscreeningshavemadeavailablemorethan106,585RNAseqsamples
(Table1)andthisnumberisconstantlyincreasing.
Table1.ThetablereportsthenumberofpubliclyavailablebulkandsinglecellRNAseqexperi
ments.Datastoredinreportedrepositoryarefrozenat
15March2021.
RepositoryURLBulkSingleCell
GDCportal.gdc.cancer.gov27,89418
ENCODEwww.encodeproject.org23237
GEOwww.ncbi.nlm.nih.gov/geo30,5102346
SRAwww.ncbi.nlm.nih.gov/sra18746428
St.Judewww.stjude.cloud3215‐
ICGCdcc.icgc.org12,840‐
GTExwww.gtexportal.org/home17,382‐
DepMapdepmap.org/portal1376‐
HumanCellAtlasdata.humancellatlas.org‐289
SingleCellPortalsinglecell.broadinstitute.org‐83
Theavailabilityofthesedataseizestheopportunitytoboostthedevelopmentof
noveldiagnostictoolsandtargetedtreatments.Indeed,theimplementationofAImodels
hasincreasedinthelast10years,asmachinelearning[13](ML)and,recently,asdeep
learning[14]methods(DL,Figure1).Theselearningmethodseffectivelyleveragethevar
iabilityofBigDatatoachieveconsistentpredictionswithouttheneedofmodelingthe
systemofinterest[15].Intheflavorofsupervised,semisupervisedandunsupervised,AI
algorithmscanbeemployedtocapturedependencies,makepredictionsandrecognize
patternsinheterogeneousdatasets[13].AIapproachesarecommonlyusedtosolvere
gression,classification,dimensionalityreductionandclusteringtasks.BeingpartofAI,
MLandDLaimatperformingtasksthatnormallyrequirehumanintelligence.MLand
DLaccomplishsimilartaskswithdistinctmathematicalapproaches.WhileMLalgorithms
stillneedhumanguidancetoimprovetheirpredictions,DLmethodscanautonomously
determinetheaccuracyofaprediction.Overall,DLispartofMLwherealgorithmsare
generallybasedonartificialneuralnetworks(NNs),theclosestrepresentationofthehu
manbrain[16](Figure2).
Figure2.Sketchrepresentingtheanalysesneededtodeciphercancerheterogeneityandachieveaneffectiveprecision
oncology.
Figure 2.
Sketch representing the analyses needed to decipher cancer heterogeneity and achieve an effective
precision oncology.
However, for an effective application of AI algorithms to cancer transcriptomics, the
availability of highly-curated datasets is fundamental [
38
]. Defining an appropriate training
dataset with well-defined features is the first important step to ensure better performance
of AI models, particularly when integrating data from different sources [
38
,
39
]. In this
view, data repositories (e.g., refine.bio, RNAseqDB) have been developed to uniformly
process and normalize cancer transcriptomes from publicly available sources [
40
,
41
]. The
use of harmonized and standardized data becomes crucial for the successful translation of
AI predictions to the clinical practice.
Finally, to boost the application of AI in precision oncology, it is of primary importance
to provide the scientific community with the code and data behind the AI model. This
practice is fundamental to ensure the reproducibility and transparency of results [
42
].
Nowadays, researchers have the opportunity to favor method shareability by implementing
them in popular machine learning frameworks, such as PyTorch [
43
] and Keras [
44
], and
uploading the trained models on dedicated repositories like Kipoi [45].
3. Managing the Heterogeneity of Cancer Transcriptomes
Heterogeneity of cancer transcriptomes can arise from both technical and biological
confounders [36]. Several strategies have been developed to increase signal-to-noise ratio
in large-scale RNA-seq datasets. In this view, a crucial step for the effectiveness of AI
algorithms is data preprocessing [
46
]. To remove the effect of confounders that can lead to
false data dependencies, techniques such as batch-correction, dimensionality reduction,
data discretization and feature selection are normally employed. These strategies can be
used independently or in combination, either as a core or a result of AI. Below, we explore
the tight link between these approaches and learning strategies. All methods that we report
are listed in Table 2and summarized in Figure 3.
Table 2. The table reports all learning approaches reported in the main text with respect to each section.
Section Method RNA-Seq Experiment Authors
Batch-correction of technical
heterogeneity
Residual neural network single-cell Shaham et al., 2017 [47]
autoencoder single-cell T. Wang et al., 2019 [48]
Autoencoder and iterative clustering single-cell Li et al., 2020 [49]
Supervised mutual nearest neighbor single-cell Yang et al., 2020 [50]
Feature extraction
Convolutional neural network bulk Elbashir et al., 2019 [51]
Convolutional neural network bulk
López-García et al., 2020 [
52
]
Deep generative models single-cell Ding et al., 2018 [53]
Wx, neural network bulk Park et al., 2019 [54]
Double Radial Basis Function Kernels bulk Liu et al., 2018 [55]
Int. J. Mol. Sci. 2021,22, 4563 5 of 18
Table 2. Cont.
Section Method RNA-Seq
Experiment Authors
Data distribution
transformation
Rank-based normalization bulk Barbie et al., 2009 [56]
GSECA, Gene Set Enrichment Class Analysis
bulk Lauria et al., 2020 [57]
Equal-width, equal-frequency binning,
k-means clustering bulk Jung et al., 2015 [58]
Data reconstruction: the
sparsity issue
AutoImpute, autoencoder single-cell Talwar et al., 2018 [59]
DeepImpute, autoencoder single-cell Arisdakessian et al., 2019 [60]
DCA, autoencoder single-cell Eraslan et al., 2019 [61]
Assessing inter-tumor
heterogeneity:
classification of cancer
subtypes
Non-negative matrix factorization bulk Wang et al., 2017 [62]
Topic modeling bulk Valle et al., 2020 [20]
Random forest bulk Alcaraz et al., 2017 [63]
Partition around medoids bulk Zhang et al., 2020 [64]
Naïve Bayes classifier bulk Paquet et al., 2015 [65]
Multiclass logistic regression bulk Cascianelli et al., 2020 [17]
DeepType, neural network bulk Chen et al., 2020 [66]
CUP-AI-Dx, convolutional neural network bulk Zhao et al., 2020 [67]
DeepCC, neural network bulk Gao et al., 2019 [18]
Defining cell types and
clones
Density clustering single-cell Izar et al., 2020 [68]
Graph-based clustering single-cell Chen et al., 2020 [21], Zhou
et al., 2020 [22]
Consensus clustering single-cell Garofano et al., 2021 [69]
DENDRO, kernel-based clustering single-cell Zhou et al., 2020 [70]
Biomarker identification
Interaction network and ridge regression bulk Kong et al., 2020 [26]
SIMMS, Interaction network and Cox
Proportional Hazards bulk Haider et al., 2018 [27]
ECMarker, Boltzman machines bulk Jin et al., 2020 [71]
Integration of ML techniques bulk
van IJzendoorn et al., 2019 [
33
]
DRjCC, non-negative matrix factorization single-cell Wu et al., 2020 [28]
maximum relevance minimum redundancy,
Support vector machine single-cell Cheng et al., 2020 [72]
Diffusion map, shared nearest-neighbor
clustering and Cox Proportional Hazards single-cell Zhang et al., 2020 [73]
Prediction of patient
survival
Cox-nnet, neural network and Cox
Proportional Hazards bulk Ching et al., 2018 [30]
DeepSurv, neural network and Cox
Proportional Hazards bulk Katzman et al., 2018 [31]
AECOX, autoencoder and Cox
Proportional Hazards, bulk Huang et al., 2020 [32]
Neural network and Cox
Proportional Hazards bulk Qiu et al., 2020 [29]
Assessment of tumor
microenvironment
CIBERSORTx, support vector regression single-cell/bulk Newman et al., 2015 [24]
EPIC, least square regression single-cell/bulk Racle et al., 2017 [74]
xCell, non-linear regression bulk Aran et al., 2017 [25]
Graph-based clustering single-cell Chen et al., 2020 [75]
K-means clustering single-cell/bulk Zhu et al., 2021 [76]
Identification of
neoepitopes
Neopepsee, Naïve Bayes, random forest,
support vector machine bulk Kim et al., 2018 [77]
MARIA, multimodal recurrent
neural network bulk Chen et al., 2019 [78]
Int. J. Mol. Sci. 2021,22, 4563 6 of 18
Int.J.Mol.Sci.2021,22,xFORPEERREVIEW6of18
Figure3.GraphicalsummaryofAIapproaches(columns)appliedtosolvetasks(rows)presented
inthisreview.CellsshowtheRNAseqdatatypeusedfortheanalysis.The“immunotherapy”
taskincludesassessmentoftumormicroenvironmentandidentificationofneoepitopes.
3.1.BatchCorrectionofTechnicalHeterogeneity
Technicalheterogeneityrisesduringtheexperimentalgenerationofsequencingdata.
Inparticular,cancertranscriptomescanbeprofiled(i)fromdistinctsampletypes;(ii)us
ingdifferentprotocolsandplatforms;and(iii)processedinunconnectedlaboratoriesby
specificusersinseparatetimes.This“batchspecific”heterogeneityconfoundsthereal
biologicalsignaloflargescaledatasets[79].Therefore,aneffectiveremovalofbatchef
fectsisanessentialstepduringdataintegration.Conventionally,batcheffectsinbulk
RNAseqareresolvedusingMLregressionmodels[80].Withtheadventofsinglecell
RNAseq,differentnonlinear,transferlearning,supervisedandunsupervisedDLap
proacheshavebeensuccessfullyproposed.Forinstance,NNstrainedtominimizedis
crepanciesbetweendistributionsofreplicateshavebeenshowntoattenuatetechnicalcon
founders[47].Autoencoders,orunsupervisedNNs,thatgraduallyremovebatcheffect
overiterationshaveshowntoamplifybiologicalsignalsbytransferringinformation
acrossbatches[48].Similarly,unsuperviseddeepembeddingNNsthatsimultaneously
learngeneexpressionrepresentationsandclusterassignmentsdemonstratedagreatre
movalofbatcheffectswhilepreservingbiologicalheterogeneity[49].Supervisedmutual
nearestneighbordetectionwithincelltypesrevealedanimprovedclusteringofcelltypes
acrossbatches[50].Overall,DLapproachesshowedthebestreductionofbatcheffectscan
beachievedwhilelearningfromclusteringdataacrossiterations.
3.2.DimensionalityReductionApproaches
Heterogeneityemergesfromthehighdimensionalityoftranscriptomicdatasets,
whichprofilethousandsofRNAisoforms.Theseprofilesresultinlistsoffixedlength
vectorsofrealvalues,namelyfeatures,whicharehighlyvariableinaspecificrange.The
extremelyhighnumberofheterogeneousfeaturespreventsdirectidentificationofbiolog
icalsimilaritiesacrosssamplesunderaphenotypeofinterest.Inthisview,dimensionality
reductionisausefulpreprocessingapproachtoremoveconfounders,speeduplearning
methodsandimprovetheiraccuracyindetectingsimilarities[81].Thesetechniquesper
formdimensionalityreductionbyeitherextractingnovelfeaturesorselectingthebestin
formativefeaturesfromtheoriginaldataset.
Figure 3.
Graphical summary of AI approaches (columns) applied to solve tasks (rows) presented in this review. Cells show
the RNA-seq data type used for the analysis. The “immunotherapy” task includes assessment of tumor microenvironment
and identification of neoepitopes.
3.1. Batch-Correction of Technical Heterogeneity
Technical heterogeneity rises during the experimental generation of sequencing
data. In particular, cancer transcriptomes can be profiled (i) from distinct sample types;
(ii) using different protocols and platforms; and (iii) processed in unconnected laboratories
by specific users in separate times. This “batch-specific” heterogeneity confounds the real
biological signal of large-scale datasets [
79
]. Therefore, an effective removal of batch-effects
is an essential step during data integration. Conventionally, batch-effects in bulk RNA-seq
are resolved using ML regression models [
80
]. With the advent of single-cell RNA-seq,
different non-linear, transfer-learning, supervised and unsupervised DL approaches have
been successfully proposed. For instance, NNs trained to minimize discrepancies be-
tween distributions of replicates have been shown to attenuate technical confounders [
47
].
Autoencoders, or unsupervised NNs, that gradually remove batch-effect over iterations
have shown to amplify biological signals by transferring information across batches [
48
].
Similarly, unsupervised deep embedding NNs that simultaneously learn gene expression
representations and cluster assignments demonstrated a great removal of batch-effects
while preserving biological heterogeneity [
49
]. Supervised mutual nearest neighbor de-
tection within cell types revealed an improved clustering of cell types across batches [
50
].
Overall, DL approaches showed the best reduction of batch-effects can be achieved while
learning from clustering data across iterations.
3.2. Dimensionality Reduction Approaches
Heterogeneity emerges from the high dimensionality of transcriptomic datasets, which
profile thousands of RNA isoforms. These profiles result in lists of fixed-length vectors of
real values, namely features, which are highly variable in a specific range. The extremely
high number of heterogeneous features prevents direct identification of biological similari-
ties across samples under a phenotype of interest. In this view, dimensionality reduction is
a useful pre-processing approach to remove confounders, speed up learning methods and
improve their accuracy in detecting similarities [
81
]. These techniques perform dimension-
Int. J. Mol. Sci. 2021,22, 4563 7 of 18
ality reduction by either extracting novel features or selecting the best informative features
from the original dataset.
3.2.1. Feature Extraction
Principal Component Analysis (PCA) is conventionally used for dimensionality re-
duction and exploratory analyses of transcriptomic profiles [
82
]. PCA aims at extracting
a limited number of new features that maximize the variability present in the original
data through a linear approach. In precision oncology, different PCA-based approaches
showed to enhance the consistency of cancer subtyping [
83
], the discovery of putative
novel therapeutic targets [84], and the identification of prognostic gene signatures [85].
Being a linear approach, the accuracy of PCA is limited when dealing with large-scale
RNA-seq datasets [
86
]. To overcome this issue, non-linear methods, such as T-distributed
Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projec-
tion (UMAP), have been recently employed to capture variability of cancer transcriptomes.
t-SNE and UMAP aim at deconvoluting relationships between neighbors in high-volume
datasets [
87
,
88
], with different implementations [
89
,
90
]. These unsupervised non-linear
dimensionality reduction approaches showed to be effective in separating cell types in
scRNA-seq datasets. Recently, these methods have also been shown to capture the hetero-
geneity of large-scale bulk RNA-seq [
91
]. Applied to thousands of cancer transcriptomes,
t-SNE revealed small gene signatures correlating with long-term survival in the majority of
tumor types [92].
Feature extraction can also be performed employing DL approaches. For instance,
convolutional NNs can automatically reduce data dimensionality and perform classifi-
cation tasks. Convolutional NN methods have been successfully implemented in digital
pathology. Interestingly, these algorithms have been capable of inferring cancer transcrip-
tomic profiles from histological images [
93
]. Applied to bulk RNA-seq datasets, CNN
revealed a high accuracy to classify cancer subtypes [
51
] and predict cancer progression [
52
].
Similarly, DL methods have been designed to improve our understanding of single-cell
heterogeneity. Deep generative models, which combine probabilistic models and NNs,
have recently been shown to enhance dimensionality reduction of scRNA-seq datasets by
preserving their global structure, thus improving the interpretation of results. Applied to a
large-scale melanoma dataset, this DL method accurately discriminated tumor cells from
microenvironment components [53].
3.2.2. Feature Selection
Feature selection (FS) aims at identifying the most important attributes from heteroge-
neous high-dimensional data [
13
]. The choice of an appropriate FS method is fundamental
for the identification of the real biological information, especially in precision oncology
when searching prognostic gene signature, biomarkers and actionable targets. In terms
of AI, the use of a list of selected features reduces overfitting effects and increases model
stability, and thus prediction accuracy. It has been shown that NN approaches improve
their accuracy when coupled with FS methods [
94
]. Similarly, regression models based on
FS resulted in an improved classification of breast cancer subtypes from bulk RNA-seq
datasets [
17
]. A review of the main feature selection approaches for bulk and single cell
transcriptomic data has been recently presented [95].
Despite being pivotal for increasing the accuracy of AI predictions, the presence of
extensive correlations between variables in large-scale datasets could reduce the stability
of selected features [
96
]. To mitigate this issue, DL-based feature selection algorithms have
been introduced. For instance, NNs have been successfully applied to identify small gene
signatures as oncogenic biomarkers from a large-scale pan-cancer RNA-seq dataset [
54
].
Similarly, the use of polynomial and radial kernels that are pivotal for ML algorithms
has been shown to achieve higher accuracy than conventional FS approaches in selecting
oncogenic gene signatures from bulk RNA-seq data [55].
Int. J. Mol. Sci. 2021,22, 4563 8 of 18
3.3. Data Distribution Transformation
Since AI methods learn from inputs to predict outputs, the different scale and distri-
bution of features in the training data can impact on model performance, particularly if the
algorithm is based on distance measures [
97
]. For instance, features with a considerable
spread of values may result in large error gradients causing NN instability [
98
]. This
holds particularly true for cancer transcriptomes where distinct genes whose expression
fluctuates in a small interval can drive the phenotype rather than single genes with large
expression spread [
99
]. Feature centering and scaling is a critical preprocessing step to
assure that all variables proportionally contribute to the AI model. These steps are widely
used to normalize bulk and single-cell RNA-seq data [
100
,
101
]. Feature scaling helps to
detect informative gene signature and altered processes from expression data. For instance,
rank-based preprocessing normalization of gene expression profiles from bulk RNA-seq
experiments has been shown to be effective in determining the real altered pathways in
KRAS-driven cancers [56].
Similarly, data discretization is a preprocessing step through which values are di-
vided in a finite number of classes. AI methods, such as classification and clustering,
can improve in learning speed and accuracy by discretizing the distribution of numer-
ical input values [
102
,
103
]. In this view, we recently demonstrated how discretization
of expression profiles boosts the prediction of altered biological processes in large-scale
transcriptomic datasets [
57
]. This preprocessing step allowed us to identify the role of
the tumor-suppressor gene, PTEN, in modulating immune-related processes and deter-
mine the maximum expression level for which PTEN leads to a worse patient survival.
Discretization of isoform-level gene expression profiles has been successfully applied to
increase accuracy of glioblastoma subtype classification [
58
]. Overall, data discretization
increases signal-to-noise ratio at the cost of a partial loss of information, which is mitigated
by the large quantity of data. Applied to transcriptomic Big Data, this technique offers the
chance to extract relevant information while accounting for their intrinsic heterogeneity.
However, due to information loss, the choice of an appropriate discretization strategy
impacts on the design and performance of the AI model, therefore remaining a non-trivial
task [104].
3.4. Data Reconstruction: The Sparsity Issue
Features that have many zero values are commonly referred to as sparse, and their
presence can lead to overfitting and reduced performances in AI models. The presence
of sparse features characterizes single-cell RNA-seq datasets due to experimental limi-
tations [
105
,
106
]. Data reconstruction aims at transforming incomplete input values into a
corresponding complete set [
107
]. Several reconstruction methods have been developed to
overcome this technical heterogeneity of single-cell transcriptomic profiles. Most of them are
autoencoder-based DL algorithms, which use probabilistic data generative processes to recon-
struct the observed profiles from low-dimensional or latent space representations [
59
61
,
105
].
The use of these data reconstruction tools has been demonstrated to enhance performance
in recovering biologically meaningful states, improving data clustering and, consequently,
differential expression analysis.
4. AI Mining of Cancer Transcriptomes
Cancer manifests its genetic heterogeneity with the presence of distinct histological
subtypes and tumor microenvironment (TME) compositions across tumors and within the
same disease. Inter- and intra-tumor heterogeneity have different clinical implications,
making their accurate identification pivotal for therapeutic decisions [
37
]. As previously
mentioned, AI models applied to transcriptomic Big Data have increased the accuracy of
cancer classification, biomarker discovery, disease recurrence and patient survival forecast,
and understanding of immune regulation.
Int. J. Mol. Sci. 2021,22, 4563 9 of 18
4.1. Assessing Inter-Tumor Heterogeneity: Classification of Cancer Subtypes
One of the most used AI approaches to assess inter-tumor heterogeneity and improve
the identification of distinct molecular subtypes using transcriptomic data is unsupervised
learning clustering. This technique aims at identifying groups of samples with similar
biological features by partitioning data according to similarity measures [
108
]. Different
studies have shown the utility of clustering approaches in detecting molecular features
(i.e., gene expression signature) responsible for patient prognosis and management. For
example, non-negative matrix factorization clustering of gene expression data has been
successfully exploited to improve ovarian cancer subtyping [
62
]. This approach identified
distinct molecular subtypes associated with different patient survival and residual disease.
Recently, topic modeling has been proposed to enhance the detection of more subtle
inter-tumor heterogeneity. Developed for natural language processing, this probabilistic
clustering algorithm aims at discovering the hidden “topics” that reflect the biological
heterogeneity and enhancing its comprehensive interpretation [
109
]. Applied to breast
and lung cancer RNA-seq datasets, topic modeling outperformed standard clustering
algorithms in identifying subtype-specific molecular features and their corresponding
clinical outcomes [20].
The use of prior information can be a useful solution to train AI models more effec-
tively [
13
]. For this reason, when prior knowledge about subtype features is available,
supervised methods can be exploited for a more accurate cancer subtyping [
17
,
18
,
63
,
65
,
66
].
For instance, feature selection of differentially expressed genes and scoring of system-level
properties (e.g., protein-protein interaction network centrality, gene essentiality, gene evolu-
tionary origin, pathway information) has been employed to select gene signatures to train
support vector machine (SVM) predictors of cancer recurrence and prognosis [
110
113
].
Similarly, the integration of pathway enrichment scores as input of random forest improved
breast cancer classification is relative to single-gene signature-based methods [
63
]. Recently,
partition around medoids clustering of metabolism-related gene set activity scores has been
used to identify prostate cancer subtypes associated with patient prognosis and therapy
response [64].
However, the heterogeneous composition of large-scale datasets can lead to AI models
that are biased toward specific subtypes, thus impacting patient management [
65
]. A Naïve
Bayes classifier based on binary rules that define gene expression dependencies within
individual samples has been proposed to improve the identification of patient-specific
tumor subtypes [
65
]. This sample-specific approach revealed an improved identification of
breast cancer subtypes, regardless of the biological (i.e., tumor cellularity) and technical
(i.e., sequencing technology) heterogeneity in the dataset. Similarly, single-sample feature
selection of gene signatures combined with multiclass logistic regression achieved the best
performance to classify breast cancer subtypes on 4731 RNA-seq expression profiles [17].
DL approaches have shown advantages over supervised ML methods for their ability
of automatically extracting features from input data [
114
]. An autoencoder-based DL
approach combining supervised classification and unsupervised clustering revealed the
presence of novel breast and bladder cancer subtypes associated with different progno-
sis [
66
]. Convolutional NNs have been employed to infer tumor’s primary tissue of origin
of metastasis and to guide management of patients with cancer of unknown primary [
67
].
Again, integrating biological information (i.e., gene set enrichment analysis) in NNs re-
sulted in an improved classification of individual colorectal and breast cancer subtypes
relative to canonical ML approaches [
18
]. A further advantage of embedding prior biologi-
cal information in AI models is the easier clinical interpretability of the features defining
different subtypes, which can foster the development of novel therapeutic strategies.
4.2. Deciphering Intra-Tumor Heterogeneity
4.2.1. Defining Cell Types and Clones
Transcriptomic profiling of single cells has allowed direct access to intra-tumor het-
erogeneity through the identification of cell types and clones composing the tumor mass.
Int. J. Mol. Sci. 2021,22, 4563 10 of 18
Learning clustering represents the commonest approach to identify gene signatures rep-
resentative of specific cell types [
21
,
22
,
68
,
69
]. As mentioned above, scRNA-seq data are
highly heterogeneous, noisy and sparse. This makes clustering analysis particularly chal-
lenging. To face this issue, dimensionality reduction approaches (e.g., principal coordinate
analysis (PCoA), t-SNE) are generally employed as preprocessing steps of clustering anal-
ysis [
21
,
22
,
68
]. These approaches, followed by a manual revision of the identified gene
signatures, have been successfully applied to identify cell types associated with differ-
ent proliferative states and therapy responses in nasopharyngeal tumors and osteosarco-
mas [
21
,
22
]. Similar to cancer subtyping, the integration of prior knowledge in learning
algorithms can be useful to improve the interpretation of intra-tumor heterogeneity. Cell
clustering using gene set features derived from enrichment analysis improved glioblas-
toma subtyping, revealing novel metabolism-associated groups associated with distinct
prognostic and therapeutic properties [
69
]. Similarly, clustering including information
about somatic alterations has been shown to improve the accuracy of subclone detection
and prediction of subclonal neoantigens in breast cancer and melanoma, respectively [
70
].
4.2.2. Assessment of TME
The heterogeneous composition of the tumor mass increases the complexity of cancer
transcriptomes, making the systematic characterization of TME fundamental for the devel-
opment of personalized therapies. The fine quantification of tumor-infiltrating immune
cells can help to guide the selection and understand the effect of immunotherapeutic
approaches. For these reasons, ML methods have been proposed to deconvolute cell-type
abundance from bulk transcriptomic profiles of mixed populations. Among others, a ML
approach based on non-negative matrix factorization has shown to accurately define cell-
type-specific expression signatures exploiting tissue heterogeneity in more than 2300 cancer
transcriptomes [
24
]. In absence of physical cell isolation, this method demonstrated to
successfully separate the contribution of malignant cells from immune cells and fibroblasts
in both head and neck tumors and melanomas. Similarly, least square regressions have
been employed to isolate cell-type-specific contributions while accounting for the presence
of uncharacterized cells in melanoma samples [
74
]. The use of gene sets rather than single
genes in a curve-fitting approach has been shown to be effective in defining expression
profiles of 60 different cell types from 9947 RNA-seq profiles across 37 cancer types [25].
The application of learning approaches to single-cell transcriptomic profiles of physi-
cally isolated cells has improved the characterization of TME composition and interactions.
PCA dimensionality reduction followed by joint embedding and clustering approach eluci-
dated the cellular composition of osteosarcoma, showing that TME-based chemotherapy
may reduce osteoclast differentiation to osteosarcoma [
22
]. UMAP-based clustering analy-
sis of scRNA-seq data unveiled the existence of novel subtypes of B cells associated with
tumor progression [75].
Finally, the combination of bulk and single-cell transcriptome profiling of tumors
can improve the characterization of TME and the selection of personalized therapeutic
treatments. For instance, supervised clustering approach of 2269 bulk and 10,434 single-
cell colorectal transcriptomes identified a TME-associated chemotherapy resistant gene
signature enabling tumor subtyping with potential therapeutic response [76].
Overall, deconvolution AI algorithms represent a powerful tool for improving our
understanding of TME composition and the delivery of personalized medicine.
4.3. Biomarker Identification
To deliver an effective personalized medicine, the precise identification of patient-
specific genetic markers that drive the disease is fundamental. To foster the discovery of
novel cancer vulnerabilities, ML and DL have been applied to large-scale transcriptomic
profiles and, often, integrated with pharmacogenomics (i.e., drug sensitivity) data. These
approaches commonly employ protein-protein interaction network-based feature selection
analyses to identify gene signatures associated with drug response, which are then used to
Int. J. Mol. Sci. 2021,22, 4563 11 of 18
train ML classifiers. Recently, an example of these ML frameworks based on ridge regres-
sions has been shown to accurately identify gene signature associated with drug response
of colorectal and bladder cancer patients [
26
]. A similar ML framework employing Cox
Proportional Hazards (Cox-PH) regression has been used to determine functional protein-
protein interaction subnetworks as prognostic biomarkers in different cancer types [
27
].
NN classifiers such as restricted Boltzmann machines have been successfully exploited to
identify biomarker gene regulatory networks, with available targeting drugs, associated
with lung cancer development [71].
The integration of multiple AI techniques can be a handy solution to improve the
identification of cancer vulnerabilities. Recently, a combination of three learning algorithms
resulted in identifying histone deacetylase inhibitors as potential therapeutic targets for
multiple soft tissue sarcomas [
33
]. In particular, the framework employed (i) NNs to
determine gene expression signatures of soft tissue sarcomas relative to healthy tissues,
(ii) random forest to identify novel diagnostic markers, and (iii) k-nearest neighbor algo-
rithm to determine prognostic genes.
The analysis of single-cell transcriptomic data can enhance the detection of gene
signatures that can discriminate between somatic cells and the other cell types composing
the tumor mass. Recently, the combination of dimensionality reduction performed by
projected matrix decomposition and clustering through non-negative matrix factoriza-
tion identified gene signatures of healthy brain cells [
28
]. Applied to bulk glioblastoma
RNA-seq data, these gene signatures successfully predicted patient survival. Similarly,
feature selection through maximum relevance minimum redundancy analysis followed
by SVM classification revealed glioblastoma-specific biomarkers associated with cancer
aggressiveness [
72
]. As described for bulk RNA-seq, the integration of prior knowledge
(e.g., protein-protein, ligand-receptor, regulatory interactions) can also improve biomarker
discovery using single-cell transcriptomic profiles. Dimensionality reduction via diffu-
sion map and shared-nearest-neighbor clustering of glioblastoma cells identified potential
prognostic biomarkers [
73
]. Overall, the identification of gene sets as biomarkers rather
than single genes provides more comprehensive information on the relevant biological
processes responsible for the disease, enlarging the list of novel putative drug targets.
4.4. Prediction of Patient Survival
Stratification of patients into groups with different survival probabilities using prog-
nostic biomarkers is pivotal to prioritize treatments and avoid unnecessary therapies [
115
].
Traditionally, the effect of gene expression on patients’ survival is measured using linear
Cox-PH regression models [
116
]. However, the high-dimensionality of transcriptomic
large-scale datasets impacts on the performance of Cox-PH models leading to overfitting
issues [
30
]. For this reason, ML extensions of the Cox-PH model employing random forest
have been developed [
117
]. Recently, NN approaches have been shown to outperform
classical survival methods [
29
32
]. These algorithms exploit feature selection through
NNs to obtain a subset of surrogate prognostic features. The surrogate features are then
used in the Cox-PH model to predict the hazard ratios. Applied to transcriptomic data
of kidney cancer, surrogate features defined by NNs have been shown to capture real
biological processes (i.e., p 53 signaling pathway) responsible for different prognosis of
patients [
30
]. The integration of prior information about drug treatments in NN prognostic
models has been shown to improve therapeutic indications according to the predicted
effect of treatment options on individual patients [
31
]. The use of autoencoders in the
feature selection step has also been proposed [32].
To address the scarcity of training samples available for specific cancer types, Cox-PH
NN generalizations can be exploited using transfer learning approaches [
14
]. Transfer
learning is a ML technique by which a model trained on one setting is exploited on another
related setting [
118
]. Transfer learning has been employed to assess patient survival in pan-
cancer RNA-seq datasets [
29
]. The model showed a higher prognostic performance than
Int. J. Mol. Sci. 2021,22, 4563 12 of 18
competing methods and exploiting risk score backpropagation [
119
] allowed to assess the
biological pathways that impact on patient’s survival outcome in the tested cancer types.
Together, these results show that NN extensions of Cox-PH modeling improve the
identification of prognostic gene signature responsible for cancer progression, and thus of
putative novel biomarkers.
4.5. Identification of Neoepitopes
Neoepitopes are tumor-specific peptides that are presented by antigen-presenting cells
through the major histocompatibility complex and recognized by the immune system [
35
].
Several promising immunotherapeutic anticancer approaches (e.g., vaccines, chimeric
antigen receptor and T-cell receptor engineered T cells) rely on the identification of suitable
target antigens. However, one of the major obstacles for the broader applicability of such
therapies is the lack of targetable tumor-specific antigens for many cancer types [
120
].
Furthermore,
in vitro
selection of antigens remains an expensive and difficult task. So far,
genomic studies analyzed thousands of sequencing data to identify somatic alterations
driving tumor progression, but only somatic mutations have been exploited for their
potential to generate novel peptides that can stimulate the immune system. Recent studies
have shown that the aberrant alternative splicing that characterizes many cancer types has
a stronger potential of generating neoepitopes [121].
NNs have been developed to improve peptide-prediction accuracy and MHC-ligand
identification [
122
] from somatic mutations. To date, these approaches have been proved
to be also effective in predicting immunogenic peptides derived from somatic splicing
defects in melanoma, B-cell lymphoma and leukemia cell lines, even if lacking clinically
relevant validations [
123
]. An ensemble of ML classifiers (i.e., Naïve Bayes, random forest
and SVM) has recently been shown to improve immunogenetic predictions of neoantigens
and proposed for ranking their potential effectiveness [
77
]. Nevertheless, the lack of
clinically validated neoantigens on large-scale cohorts limits the efficient training of AI
algorithms [
124
]. To solve this issue, a multimodal recurrent NNs approach integrating
mass spectrometry data has been proposed [
78
]. Overall, the identification of neoantigens
from large-scale transcriptomic dataset is still in its infancy and presents considerable
opportunities for AI improvements.
5. Conclusions
Despite the results achieved so far, the application of AI to cancer transcriptome Big
Data for valuable precision oncology is still limited. The complexity of cancer heterogeneity
remains the major challenge to disentangle. On the one hand, AI represents the most pow-
erful tool to extract the real biological information from large-scale transcriptomic datasets.
As national and international sequencing consortia generate sequencing data, the ability of
DL algorithms to capture the hidden relationships responsible for a phenotype without
requiring a human supervision will become pivotal for our understanding of diseases
and guide personalized therapeutic interventions. On the other hand, AI data mining
poses several challenges. Harnessing Big Data carries with it the ‘curse of dimensional-
ity’ phenomenon, or the need of more data when information increases [
125
,
126
]. When
dimensionality grows, data becomes sparse. Any sample is likely to be more separated
from its neighbors at the increase of the space dimensionality. Hence, having data fully
representative of the heterogeneity of a phenotype will become more and more complicated
as the variables of interest will increase. This holds particularly true for cancer types that
are rare and heterogeneous. Dimensionality reduction methods are a solution to mitigate
the curse of dimensionality. Similarly, data discretization approaches can help to reduce
dimensionality supporting the paradigm of “less is more”. Despite being powerful tools,
AI approaches require tailor-made designs to achieve good performances and biologically
relevant results. The “black-box” nature of learning algorithms needs to be fully exploited
to reach a comprehensive understanding of the cancer phenotype of interest. Improving
the interpretability of results of AI models remains an important challenge [
127
], especially
Int. J. Mol. Sci. 2021,22, 4563 13 of 18
when selecting for therapeutic treatments. However, the integration of prior biological
knowledge into the algorithms can guide toward this direction. Combining data from multi
omics approaches will provide a deeper understanding of cancer heterogeneity. However,
new AI methods will be required to face the resulting curse of dimensionality. Of note, part
of cancer transcriptomic data originates from preclinical research employing cell lines and
mouse models. Despite the undeniable value of these data, molecular differences between
these models and patient tumors call for caution in extending results to the human sys-
tem [
128
,
129
]. Therefore, approaches aiming at delineating the similarities and differences
between preclinical and clinical transcriptomes are required for an effective application of
AI to improve the patient’s quality of life [130,131].
The demand of AI in precision oncology will go hand in hand with the need of doctors
and experts that will be able to translate results into real precision therapeutic decisions
and participate actively in the development of learning strategies. In this light, a precision
AI-driven oncology will become effectively available on demand.
Author Contributions:
Conceptualization, M.D.G., S.P. (Serena Peirone) and M.C.; data curation,
M.D.G., S.P. (Serena Peirone), S.P. (Sarah Perrone) and F.P.; writing—original draft preparation,
M.D.G., S.P. (Serena Peirone), S.P. (Sarah Perrone), F.P., F.V. and M.C.; writing—review and editing,
M.D.G., S.P. (Serena Peirone), E.T., F.F. and M.C.; visualization, M.C.; supervision, M.C.; project
administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published
version of the manuscript.
Funding:
The research leading to these results has received funding from AIRC under MFAG 2017—
ID. 20566 project—P.I. Cereda Matteo. Funding for open access charge: Italian Association for Cancer
Research. MC is supported by the “Compagnia di San Paolo” institutional grant.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
A tutorial to implement AI algorithms in the R scripting language for
sample classification and gene signature discovery is available at http://www.ceredalab/AI/index.
html and at https://github.com/matteocereda/AI, accessed on 24 April 2021.
Acknowledgments:
We sincerely thank Giuseppe Basso (1948–2021) for his guidance, teachings, and
dedication to foster a research aimed at the needs of patients.
Conflicts of Interest: The authors declare no conflict of interest.
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... If linear separation is not applicable, the kernel approach can be applied to transform the training samples into a high-dimensional space. A separator is then used in learning to this space [42]. It stands out amongst other known classification approaches based on computational circumstances over their opponents. ...
... SVMs control non-linear decision margins of unpredictable intricacy. Linear SVMs are used for specific linear discriminant classifications [42]. Linear SVM applies as a maximum margin classifier when the datasets are linearly distinguishable. ...
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... Auslander et al. reviewed machine learning/deep learning approaches incorporated to establish bioinformatics and computational biology frameworks in the areas of molecular evolution, protein structure analysis, systems biology, and disease genomics [19]. Del Giudice et al. comprehensively reviewed machine learning/deep learning solutions for computational problems in bulk and single-cell RNA-sequencing data analysis [20]. Banegas-Luna et al. discussed the interpretability of machine learning/deep learning methods in cancer research [21]. ...
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