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Performance of knowledge-based biological models in higher dimensional chemical space

Kavitha, AP and Jaleel, Abdul UC and Mujeeb, Abdul VM and Muraleedharan, K (2016) Performance of knowledge-based biological models in higher dimensional chemical space. In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 153 . pp. 58-66.

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Official URL: http://dx.doi.org/10.1016/j.chemolab.2016.02.009

Abstract

This study evaluates the improvement of the knowledge-based biological models by incorporating additional advanced molecular descriptors to the existing classical descriptors. It was found that the inclusion of constitutional, topological, and hybrid descriptors in the generation of biological models trained on Mtb (Mycobacterium tuberculosis) bioassay dataset using classifiers like Random Forest, J48, Naive Bayes, and SMO (Sequential Minimal Optimization) have found to enhance the performance of these models. (C) 2016 Elsevier B.V. All rights reserved.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
Keywords: Artificial intelligence; Data mining; Machine learning; Predictive modeling; Molecular descriptors; Anti-tubercular activity
Department/Centre: Division of Biological Sciences > Biochemistry
Depositing User: Id for Latest eprints
Date Deposited: 11 Jun 2016 06:25
Last Modified: 26 Oct 2018 14:44
URI: http://eprints.iisc.ac.in/id/eprint/53899

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