NDLI: Evaluating the denoising techniques in protein-protein interaction prediction

Content Provider	IEEE Xplore Digital Library
Author	Yong-Cui Wang Xian-Wen Ren Chun-Hua Zhang Nai-Yang Deng Xiang-Sun Zhang
Copyright Year	2011
Description	Author affiliation: Information School, Renmin University of China, Beijing, China, 100872 (Chun-Hua Zhang) \|\| State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences and Peking Union Medical College, Beijing, China, 100730 (Xian-Wen Ren) \|\| College of Science, China Agricultural University, Beijing, China, 100083 (Nai-Yang Deng) \|\| Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, 100190 (Xiang-Sun Zhang) \|\| Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining, China, 810001 (Yong-Cui Wang)
Abstract	The past decades witnessed extensive efforts to study the relationships among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. The composition vectors are usually constructed to encode proteins as real-value vectors, which is feeding to a machine learning framework. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vector. Thus formulation to estimate the noise may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which are efficient in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E.coli) and Saccharomyces cerevisiae (S.cerevisiae) randomly and artificial negative datasets, respectively, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that, the denoising formulation efficient in phylogenetic trees construction can not improve the PPIs prediction, that is, what is noise is dependent on the applications.
Starting Page	78
Ending Page	83
File Size	586439
Page Count	6
File Format	PDF
ISBN	9781457716614
e-ISBN	9781457716669
e-ISBN	9781457716652
DOI	10.1109/ISB.2011.6033124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Support vector machines Noise reduction Noise Tin Amino acids Phylogeny
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Prediction of protein-protein interactions through support vector machines

Combining GOR techniques with support vector machines for protein secondary structure prediction

Prediction of protein-protein interaction types with amino acid index distribution and pairwise kernel SVM

Prediction of crystal packing and biological protein-protein interactions

Prediction of Protein-Protein Interaction Using Distance Frequency of Amino Acids Grouped with their Physicochemical Properties

Protein-protein interaction prediction using desolvation energies and interface properties

Protein secondary structure prediction using support vector machines and a codon encoding scheme

Dataset reconstruction for protein interface identification using manifold learning method

Sensitive prediction of bacterial type IV effectors

Evaluating the denoising techniques in protein-protein interaction prediction

Similar Documents

Prediction of protein-protein interactions through support vector machines

Combining GOR techniques with support vector machines for protein secondary structure prediction

Prediction of protein-protein interaction types with amino acid index distribution and pairwise kernel SVM

Prediction of crystal packing and biological protein-protein interactions

Prediction of Protein-Protein Interaction Using Distance Frequency of Amino Acids Grouped with their Physicochemical Properties

Protein-protein interaction prediction using desolvation energies and interface properties

Protein secondary structure prediction using support vector machines and a codon encoding scheme

Dataset reconstruction for protein interface identification using manifold learning method

Sensitive prediction of bacterial type IV effectors

Evaluating the denoising techniques in protein-protein interaction prediction