Drug-target matrix.

Drug-target matrix.

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Cancer is one of the most influential factors causing death in the world. Adenosine which is a molecule, found in all human cells by coupling with G protein it turns into an adenosine receptor. Adenosine receptor is an important target for cancer therapy. Adenosine stops the growth of malignant tumor cells such as lymphoma, melanoma and prostate ca...

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... the processing phase, we combined both datasets and generated a new dataset as mentioned earlier in TABLE 2. After that we labeled (represented by a random number) each drug, target and drug side effect then each label was encoded. Lastly, we started building matrices, the first matrix represented drug-target pairs where a drug (D1) interacts with one target or more (Tk) integrating to this matrix the drug fingerprint as shown in Figure 3. The second matrix represented drug-side effect pairs where a drug (D1) had several side effects (Sm) as shown in Figure 4. ...

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... While there have been several previous attempts to use machine learning for ARs (Saad et al., 2019;Wang et al., 2021), few have performed external validation. One recent study used deep learning combined with pharmacophore and docking approaches to identify novel A 1 /A 2A antagonists (Wang et al., 2021). ...
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Adenosine (ADO) is an extracellular signaling molecule generated locally under conditions that produce ischemia, hypoxia, or inflammation. It is involved in modulating a range of physiological functions throughout the brain and periphery through the membrane-bound G protein-coupled receptors, called adenosine receptors (ARs) A1AR, A2AAR, A2BAR, and A3AR. These are therefore important targets for neurological, cardiovascular, inflammatory, and autoimmune diseases and are the subject of drug development directed toward the cyclic adenosine monophosphate and other signaling pathways. Initially using public data for A1AR agonists we generated and validated a Bayesian machine learning model (Receiver Operator Characteristic of 0.87) that we used to identify molecules for testing. Three selected molecules, crisaborole, febuxostat and paroxetine, showed initial activity in vitro using the HEK293 A1AR Nomad cell line. However, radioligand binding, β-arrestin assay and calcium influx assay did not confirm this A1AR activity. Nevertheless, several other AR activities were identified. Febuxostat and paroxetine both inhibited orthosteric radioligand binding in the µM range for A2AAR and A3AR. In HEK293 cells expressing the human A2AAR, stimulation of cAMP was observed for crisaborole (EC50 2.8 µM) and paroxetine (EC50 14 µM), but not for febuxostat. Crisaborole also increased cAMP accumulation in A2BAR-expressing HEK293 cells, but it was weaker than at the A2AAR. At the human A3AR, paroxetine did not show any agonist activity at 100 µM, although it displayed binding with a Ki value of 14.5 µM, suggesting antagonist activity. We have now identified novel modulators of A2AAR, A2BAR and A3AR subtypes that are clinically used for other therapeutic indications, and which are structurally distinct from previously reported tool compounds or drugs.
... To overcome challenges of imbalanced data (He and Garcia, 2009;Fernandez et al. 2017;Leevy et al. 2018), one popular method to rebalance training data is Synthetic Minority Oversampling Technique (SMOTE) (Chawla et al. 2002). SMOTE has shown good results in improving the capability of machine learning models in imbalanced applications such as drug trials (Saad et al 2019) and driving risk classification , and therefore it has been implemented in this case. SMOTE searches k-nearest minority neighbours of each minority instance, selecting one of the neighbours as a reference point and generating a new value by multiplying the difference with a random value between 0 and 1 (r). ...
Thesis
Shipping is an essential component of the global economy, but every year accidents result in significant loss of life and environmental pollution. Navigating vessels might collide with one another, run aground or capsize amongst a multitude of challenges to operating at sea. As the number and sizes of vessels have increased, novel or autonomous technologies are adopted and new environments such as the Arctic are exploited, these risks are likely to increase. Coastal states, ports and developers have a responsibility to assess these risks, and where the risk is intolerably high, implement mitigation measures to reduce them. To support this, significant research has developed a field of maritime risk analysis, attempting to employ rigorous scientific study to quantifying the risk of maritime accidents. Such methods are diverse, yet have received criticism for their lack of methodological rigour, narrow scope and one-dimensional rather than spatial-temporal approach to risk. More broadly, there is a recognition that by combining different datasets together, novel techniques might lead to more robust and practicable risk analysis tools. This thesis contributes to this purpose. It argues that by integrating massive and heterogenous datasets related to vessel navigation, machine learning algorithms can be used to predict the relative likelihood of accident occurrence. Whilst such an approach has been adopted in other disciplines this remains relatively unexplored in maritime risk assessment. To achieve this, four aspects are investigated. Firstly, to enable fast and efficient integration of different spatial datasets, the Discrete Global Grid System has been trialled as the underlying spatial data structure in combination with the development of a scalable maritime data processing pipeline. Such an approach is shown to have numerous advantageous qualities, particular relevant to large scale spatial analysis, that addresses some of the limitations of the Modifiable Areal Unit Problem. Secondly, a national scale risk model was constructed for the United States using machine learning methods, providing high-resolution and reliable risk assessment. This supports both strategic planning of waterways and real-time monitoring of vessel transits. Thirdly, to overcome the infrequency of accidents, near-miss modelling was undertaken, however, the results were shown to only have partial utility. Finally, a comparison is made of various conventional and machine methodologies, identifying that whilst the latter are often more complex, they address some failings in conventional methods. The results demonstrate the potential of these methods as a novel form of maritime risk analysis, supporting decision makers and contributing to improving the safety of vessels and the protection of the marine environment.
... where D new is the synthetic sample, D i are minority samples, D knn a sample of k-nearest neighbour from minority samples and rand is a random number between 0 and 1 [49]. ...
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Electronic Health Records (EHRs) hold symptoms of many diverse diseases and it is imperative to build models to recognise these problems early and classify the diseases appropriately. This classification task could be presented as a single or multi-label problem. Thus, this study presents Psychotic Disorder Diseases (PDD) dataset with five labels: bipolar disorder, vascular dementia, attention-deficit/hyperactivity disorder (ADHD), insomnia, and schizophrenia as a multi-label classification problem. The study also investigates the use of deep neural network and machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF) and Decision tree (DT), for identifying hidden patterns in patients’ data. The study furthermore investigates the symptoms associated with certain types of psychotic diseases and addresses class imbalance from a multi-label classification perspective. The performances of these models were assessed and compared based on an accuracy metric. The result obtained revealed that deep neural network gave a superior performance of 75.17% with class imbalance accuracy, while the MLP model accuracy is 58.44%. Conversely, the best performance in the machine learning techniques was exhibited by the random forest model, using the dataset without class imbalance and its result, compared with deep learning techniques, is 64.1% and 55.87%, respectively. It was also observed that patient’s age is the most contributing feature to the performance of the model while divorce is the least. Likewise, the study reveals that there is a high tendency for a patient with bipolar disorder to have insomnia; these diseases are strongly correlated with an R-value of 0.98. Our concluding remark shows that applying the deep and machine learning model to PDD dataset not only offers improved clinical classification of the diseases but also provides a framework for augmenting clinical decision systems by eliminating the class imbalance and unravelling the attributes that influence PDD in patients..
... SVM is a binary classifier based on the idea of hyperplanes separation of objects. Hyperplanes act as a boundary to distinguish data points to be assigned to different classes [12]. ...
... If there is a class imbalance, the accuracy of the classifier decreases. To overcome this problem, we have used oversampling method called Synthetic Minority Oversampling technique (SMOTE) to balance our dataset [20]. Too much oversampling results in overfitting problem, so that we have not applied SMOTE to the test set. ...
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Vitamin D Deficiency (VDD) is one of the most significant global health problem and there is a strong demand for the prediction of its severity using non-invasive methods. The primary data containing serum Vitamin D levels were collected from a total of 3044 college students between 18-21 years of age. The independent parameters like age, sex, weight, height, body mass index (BMI), waist circumference, body fat, bone mass, exercise, sunlight exposure, and milk consumption were used for prediction of VDD. The study aims to compare and evaluate different machine learning models in the prediction of severity in VDD. The objectives of our approach are to apply various powerful machine learning algorithms in prediction and evaluate the results with different performance measures like Precision, Recall, F1-measure, Accuracy, and Area under the curve of receiver operating characteristic (ROC). The McNemar’s test was conducted to validate the empirical results which is a statistical test. The final objective is to identify the best machine learning classifier in the prediction of the severity of VDD. The most popular and powerful machine learning classifiers like K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), AdaBoost (AB), Bagging Classifier (BC), ExtraTrees (ET), Stochastic Gradient Descent (SGD), Gradient Boosting (GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) were implemented to predict the severity of VDD. The final experimentation results showed that the Random Forest Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. The McNemar’s statistical test results support that the RF classifier outperforms than the other classifiers.
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Extreme weather events can result in loss of life, environmental pollution and major damage to vessels caught in their path. Many methods to characterise this risk have been proposed, however, they typically utilise deterministic thresholds of wind and wave limits which might not accurately reflect risk. To address this limitation, we investigate the potential of machine learning algorithms to quantify the relative likelihood of an incident during the US Atlantic hurricane season. By training an algorithm on vessel traffic, weather and historical casualty data, accident candidates can be identified from historic vessel tracks. Amongst the various methods tested, Support Vector Machines showed good performance with Recall at 95% and Accuracy reaching 92%. Finally, we implement the developed model using a case study of Hurricane Matthew (October 2016). Our method contributes to enhancements in maritime safety by enabling machine intelligent risk-aware ship routing and monitoring of vessel transits by Coastguard agencies.