Machine Learning based Predictive Model for Screening Mycobacterium Tuberculosis Transcriptional Regulatory Protein Inhibitors from High-Throughput Screening Dataset

Abstract

In view of the essential role played by dosRS in the survival of Mycobacterium in the infected granuloma cells, dosRS transcriptional regulatory proteins were considered as a validated target for high throughput screening (HTS). However, the cost and time factor involved in screening large compound libraries are an important hurdle in identifying lead compounds. Therefore, the use of computational machine learning techniques to build a predictive model for screening putative drug-like molecule has gained significance. In this regard, a target-based predictive model using machine learning approaches was built to develop fast and efficient virtual screening procedures to screen anti-dosRS molecules. In the present study, we have used various structural and physiochemical attributes of compounds from HTS dataset to train and build a chemoinformatics predictive model based on four state-of-art supervised classifiers (Random forest, SMO, J48, and Naïve Bayes). The trained model was applied to test dataset for validating the robustness, accuracy, and sensitivity of the predictive model in screening active anti-dosRS molecules. The Cost-Sensitive Classifier (CSC) with Random Forest (RF) algorithm based predictive model showed a high sensitivity (100%) and specificity (83.13%) to identify active and inactive molecules, respectively from assay dataset (ID: 1159583). CSC-RF proved to more robust and efficient in classifying active molecule from an imbalanced dataset with highest Balancing Classification Rate (BCR) (91.57%) and maximum Area under the Curve (AUC) value (0.999).

Authors and Affiliations

Syed Asif Hassan, Tabrej Khan

Keywords

Related Articles

COMPARATIVE STUDY OF THE SOFTWARE METRICS FOR THE COMPLEXITY AND MAINTAINABILITY OF SOFTWARE DEVELOPMENT

Software metrics is one of the well-known topics of research in software engineering. Metrics are used to improve the quality and validity of software systems. Research in this area focus mainly on static metrics obtaine...

Challenges in Designing Ethical Rules for Infrastructures in Internet of Vehicles

Vehicular Ad-hoc Networks (VANETs) have seen significant advancements in technology. Innovation in connectivity and communication has brought substantial capabilities to various components of VANETs such as vehicles, inf...

Review of Cross-Platforms for Mobile Learning Application Development

Mobile learning management systems are very important for training purpose. But considering the present scenario, the learners are equipped with a number of mobile devices that run by different operating systems with div...

Iterative Removing Salt and Pepper Noise based on Neighbourhood Information

Denoising images is a classical problem in low-level computer vision. In this paper, we propose an algorithm which can remove iteratively salt and pepper noise based on neighbourhood while preserving details. First, we c...

PSIM and MATLAB Co-Simulation of Photovoltaic System using “P and O” and “Incremental Conductance” MPPT

The photovoltaic (PV) generator shows a nonlinear current-voltage (I-V) characteristic that its maximum power point (MPP) differs with irradiance and temperature. by employing simple maximum power point tracking algorith...

Download PDF file
  • EP ID EP258304
  • DOI 10.14569/IJACSA.2017.081215
  • Views 107
  • Downloads 0

How To Cite

Syed Asif Hassan, Tabrej Khan (2017). Machine Learning based Predictive Model for Screening Mycobacterium Tuberculosis Transcriptional Regulatory Protein Inhibitors from High-Throughput Screening Dataset. International Journal of Advanced Computer Science & Applications, 8(12), 116-123. https://europub.co.uk/articles/-A-258304