A Machine Learning Approach for Drug‐target Interaction Prediction using Wrapper Feature Selection and Class Balancing

2020 ◽  
Vol 39 (5) ◽  
pp. 1900062 ◽  
Author(s):  
Shweta Redkar ◽  
Sukanta Mondal ◽  
Alex Joseph ◽  
K. S. Hareesha
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Drug Target ◽  
Learning Approach ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Machine Learning Approach ◽  
Wrapper Feature Selection

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction

2019 ◽  
Vol 20 (3) ◽  
pp. 194-202 ◽  
Author(s):  
Wen Zhang ◽  
Weiran Lin ◽  
Ding Zhang ◽  
Siman Wang ◽  
Jingwen Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Prediction Models ◽  
Prediction Methods ◽  
Crucial Issue ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Recent Advances ◽  
Drug Similarity

Background:The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.Results:In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods.Conclusion:This study provides the guide to the development of computational methods for the drug-target interaction prediction.

scholarly journals A machine learning approach for feature selection traffic classification using security analysis

2018 ◽  
Vol 74 (10) ◽  
pp. 4867-4892 ◽  
Author(s):  
Muhammad Shafiq ◽  
Xiangzhan Yu ◽  
Ali Kashif Bashir ◽  
Hassan Nazeer Chaudhry ◽  
Dawei Wang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Security Analysis ◽  
Learning Approach ◽  
Traffic Classification ◽  
Machine Learning Approach

scholarly journals Machine learning prediction of oncology drug targets based on protein and network properties

2019 ◽  
Author(s):  
Zoltan Dezso ◽  
Michele Ceccarelli
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Drug Target ◽  
Drug Targets ◽  
Validation Dataset ◽  
Learning Approach ◽  
Biological Functions ◽  
Machine Learning Approach ◽  
Network Properties ◽  
Trial Drug

Abstract Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an AUC of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

scholarly journals Machine learning prediction of oncology drug targets based on protein and network properties

2019 ◽  
Author(s):  
Zoltan Dezso ◽  
Michele Ceccarelli
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Drug Target ◽  
Drug Targets ◽  
Validation Dataset ◽  
Learning Approach ◽  
Biological Functions ◽  
Machine Learning Approach ◽  
Network Properties ◽  
Trial Drug

Abstract Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an AUC of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

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