NDLI: Protein secondary structure prediction based on improved SVM method in compound pyramid model

Content Provider	IEEE Xplore Digital Library
Author	Bingru Yang Wu Qu Yun Zhai Haifeng Sui
Copyright Year	2010
Description	Author affiliation: School of Information Engineering, University of Science and Technology Beijing, 100083, China (Bingru Yang; Wu Qu; Yun Zhai; Haifeng Sui)
Abstract	Methods for predicting protein secondary structure provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. Secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a method of improved SVM for predicting protein secondary structure. Using evolutionary information contained in amino acid's physicochemical properties, position-specific scoring matrix generated by psi-blast as input to improved SVM, secondary structure can be predicted at significantly increased accuracy. Based on KDTICM theory, we have constructed a compound pyramid model, which is composed of four layers of the intelligent interface and integrated in several ways, such as improved SVM, mixed-modal BP, KDD* method and so on. On the RS126 data set, state overall per-residue accuracy, Q3 reached 83.06%, while SOV99 accuracy increased to 80.6%.On the CB513 data set, Q3 reached 80.49%, SOV99 accuracy increased to 79.84%.This article briefly introduces this model and highlights the improved SVM method.
Starting Page	4405
Ending Page	4410
File Size	510975
Page Count	6
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Protein engineering Materials testing Sequences Biological system modeling Prediction methods Predictive models Amino acids Educational institutions SVM Support vector machines Accuracy Compound Pyramid Model Protein Second Structure
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

An approach of protein secondary structure prediction based on SVM method in compound pyramid model

A Novel Protein Secondary Structure Prediction System Based on Compound Pyramid Model

An novel method of protein secondary structure prediction based on compound pyramid model

An improved CBA prediction algorithm in compound pyramid model

Protein secondary structure prediction using an evolutionary computation method and clustering

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

An approach of protein secondary structure prediction based on homology analysis method in compound pyramid model

Predicting Protein Secondary Structure Based on Compound Pyramid Model

Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules

Protein secondary structure prediction based on improved SVM method in compound pyramid model

Similar Documents

An approach of protein secondary structure prediction based on SVM method in compound pyramid model

A Novel Protein Secondary Structure Prediction System Based on Compound Pyramid Model

An novel method of protein secondary structure prediction based on compound pyramid model

An improved CBA prediction algorithm in compound pyramid model

Protein secondary structure prediction using an evolutionary computation method and clustering

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

An approach of protein secondary structure prediction based on homology analysis method in compound pyramid model

Predicting Protein Secondary Structure Based on Compound Pyramid Model

Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules

Protein secondary structure prediction based on improved SVM method in compound pyramid model