Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.

Abstract:

:Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

journal_name

Amino Acids

journal_title

Amino acids

authors

Shi MG,Xia JF,Li XL,Huang DS

doi

10.1007/s00726-009-0295-y

subject

Has Abstract

pub_date

2010-03-01 00:00:00

pages

891-9

issue

3

eissn

0939-4451

issn

1438-2199

journal_volume

38

pub_type

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