An Empirical Study to Analyse The Effect of Bagging and Feature Subspacing on The Performance of A Custom Ensemble Algorithm for Predicting Drug Protein Interactions

Harshita Bhargava; Amita Sharma; Prashanth Suravajhala

doi:10.17485/IJST/v17i10.3202

Article

An Empirical Study to Analyse The Effect of Bagging and Feature Subspacing on The Performance of A Custom Ensemble Algorithm for Predicting Drug Protein Interactions

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i10.3202

Year: 2024, Volume: 17, Issue: 10, Pages: 911-916

Original Article

An Empirical Study to Analyse The Effect of Bagging and Feature Subspacing on The Performance of A Custom Ensemble Algorithm for Predicting Drug Protein Interactions

Harshita Bhargava^1*, Amita Sharma¹, Prashanth Suravajhala^2,3

¹Department of Computer Science & IT, IIS (deemed to be University), Jaipur, Rajasthan, India
²Amrita School of Biotechnology, Amrita University, Clappana, Kollam, 690525, Kerala, India
³Bioclues.org, India

*Corresponding Author
Email: [email protected]

Received Date:12 December 2023, Accepted Date:30 January 2024, Published Date:27 February 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The objective of this study is to analyse the effect of bagging and feature subspacing on the performance of a custom ensemble of decision tree classifiers for predicting drug protein interactions. Methods: In our present work we have designed a custom ensemble algorithm with decision trees as the base learner. We analysed the effect of bagging negative samples and feature subspacing on the performance of the custom ensemble in terms of AUCROC and AUPR. The Enzyme dataset from the Yamanishi dataset composed of 445 drugs and 664 proteins was used for the experiments. Findings: It was observed that the effect of bagging negative samples was significant as compared to feature supspacing in terms of AUPR metric. Now since AUPR is a metric that remains unaffected by the presence of negative samples hence the increase in AUPR by increasing the negative to positive ratio clearly indicated that the negative samples do contain the positives which are unknown and are yet to be verified. Novelty: The results give a strong indication that that feature subspacing has no considerable impact on the AUCROC metric performance of the custom ensemble while AUPR metric increases as the negative to positive ratio increases. The results give a foundation to the fact that, finding reliable negative samples from the entire set of negative drug protein pairs can further enhance the performance of the machine learning classifiers.

Keywords: Decision tree classifier, Ensemble classifier, Drug discovery, Bagging, Drug repurposing

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Copyright

© 2024 Bhargava et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)