Sequence-based protein-protein interaction prediction optimized for target selection in biological experiments

Ye Jiankuan, Casimir Kulikowski, Ilya Muchnik

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

1 Scopus citations

Abstract

A set of protein pairs predicted to be interacting with high ratio of true positive is valuable for target selection in experiments like protein structure determination. Our goal in this paper is to investigate the problem of finding such a set of protein pairs in an organism by machine learning methods. Yeast genome was taken for this study and support vector machine was adopted as the classification model. Domain information of each protein was extracted and transformed into features of a protein pair. We specifically analyzed the effect of negative sample selection based on different principles. We also evaluated the feasibility to adjust the intercept parameter of a trained SVM model to improve the ratio of predicted true positive. Our result shows that the approximate 1:3 ratio of positive samples to negative ones in the testing data can be significantly improved to 2:1 of the positive to negative in the predicted data.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages236-239
Number of pages4
StatePublished - 2005
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/TerritoryChina
CityShanghai
Period9/1/059/4/05

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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