Sequence-based prediction of protein-protein interactions using pseudo substitution matrix representation features and ensemble rotation forest classifier in HIV (human immunodeficiency virus)

Dian Lestari, S. Hartomo, Alhadi B.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set of interacting human and viral proteins would greatly contribute to our understanding of the mechanisms of infection and then to design of new therapeutic approaches. Predicting Protein-Protein Interaction (PPI) is important for making discoveries in the molecular mechanisms within a cell. Sequence-based prediction is the most readily applicable and effective cost method to predict protein-protein interactions. By using computation processes and applying machine learning methods, it is more efficient than conventional method which takes a long time and expensive cost. In this study, we used pseudo-substitution matrix representation as a feature extraction method, after that we used rotation forest ensemble classifier to predict protein-protein interactions class between humans proteins and HIV proteins. The experiment results show that proposed method is more feasible, powerful and can be improved to predict other protein-protein interactions.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • Classifier combined
  • global encoding
  • rotation forest

Fingerprint Dive into the research topics of 'Sequence-based prediction of protein-protein interactions using pseudo substitution matrix representation features and ensemble rotation forest classifier in HIV (human immunodeficiency virus)'. Together they form a unique fingerprint.

Cite this