Predicting Protein-Protein Interactions from Protein Sequence Using Locality Preserving Projections and Rotation Forest

Xinke Zhan, Zhuhong You, Changqing Yu, Jie Pan, Ruiyang Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Protein-protein interactions (PPIs) play an important role in nearly every aspect of the cell function in biological system. A number of high-throughput technologies have been proposed to detect the PPIs in past decades. However, they have some drawbacks such as time-consuming and high cost, and at the same time, a high rate of false positive is also unavoidable. Hence, developing an efficient computational method for predicting PPIs is very necessary and urgent. In this paper, we propose a novel computational method for predicting PPIs from protein sequence using Locality Preserving Projections (LPP) and Rotation Forest (RF) model. Specifically, the protein sequence is firstly transformed into Position Specific Scoring Matrix (PSSM) generated by multiple sequences alignments. Then, the LPP descriptor is applied to extract protein evolutionary information from. Finally, the RF classifier is adopted to predict whether the given protein pair is interacting or not. When the proposed method performed on Yeast and H. pylori PPIs datasets, it achieved the results with an average accuracy of 92.52% and 91.46%, respectively. To further verify the performance of the proposed method, we compare the proposed method with the state-of-the-art support vector machine (SVM) and get good results. The promising results indicated the proposed method is stable and robust for predicting PPIs.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings
EditorsDe-Shuang Huang, Kang-Hyun Jo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages121-131
Number of pages11
ISBN (Print)9783030608019
DOIs
StatePublished - 2020
Externally publishedYes
Event16th International Conference on Intelligent Computing, ICIC 2020 - Bari , Italy
Duration: 2 Oct 20205 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12464 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Intelligent Computing, ICIC 2020
Country/TerritoryItaly
CityBari
Period2/10/205/10/20

Keywords

  • Locality preserving projections
  • PSSM
  • Rotation forest

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