Integrating data selection and extreme learning machine to predict protein-ligand binding site

Umi Mahdiyah, Elly Matul Imah, M. Isa Irawan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Recently, computer-aided drug design is developing rapidly. The first step of computer-aided drug design is to find a protein - ligand binding site, which is a pocket or cleft on the surface of the protein being used to bind a ligand (drug). In this study, the binding site is defined as a binary classification problem to differ the location which can bind or cannot bind the ligand. Classification method used in this research is Extreme Learning Machine (ELM), because this method has fast learning process. In the real case, the dataset usually has imbalanced data. One of them is to predict binding site. Imbalanced data can be solved in several ways. In this study we carried out the integration of data selection and classification to overcome the inconsistency problem. The performance of integrating between data selection and Extreme Learning Machine to predict protein-ligand binding site is measured by using recall, specificity, G-mean and CPU time. The average of recall, specificity, G-mean and CPU time in this research are respectively, those are 91.8472%, 97.071%, 94.2647 %, and 2.79 second.

Original languageEnglish
Pages (from-to)791-797
Number of pages7
JournalContemporary Engineering Sciences
Volume9
Issue number13-16
DOIs
Publication statusPublished - 2016

Keywords

  • Binding site protein-ligand
  • Extreme learning machine
  • Imbalanced data

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