NDLI: Predicting Membrane Protein Types with Dimensionality Reduction and Kernel Method

Content Provider	IEEE Xplore Digital Library
Author	Li-Peng Wang Zhan-Ting Yuan Xu-Hui Chen Zhi-Fang Zhou
Copyright Year	2009
Abstract	The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. How to fast and efficiently annotate the type of an uncharacterized membrane protein is a challenge. Some discrete models, such as DC (Dipeptide Composition) have been proposed to represent a protein sequence in the field of predicting membrane protein types. However, a high dimensional disaster may be caused by using this representation method. In this paper, a linear dimensionality reduction algorithm LDA (Linear Discriminant Analysis) and a nonlinear dimensionality reduction algorithm KLDA (Kernel Linear Discriminant Analysis) are introduced to solve this problem by extracting the indispensable features from the high-dimensional DC space, respectively. Based on the reduced low-dimensional features, K-NN (K-nearest neighbor) classifier is introduced to identify the types of membrane proteins. As a result, experiment results show that using the proposed method to cope with prediction of membrane proteins types is very effective. It also can be seen that the success rates obtained by LDA are higher than those by other dimensionality reduction method such as PCA.
Starting Page	71
Ending Page	74
File Size	323859
Page Count	4
File Format	PDF
ISBN	9780769537351
DOI	10.1109/FSKD.2009.721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Protein engineering Membrane protein Dimensionality reduction method LDA Lipidomics Educational institutions Amino acids Kernel method Feature extraction Biomembranes Protein sequence Linear discriminant analysis Kernel KLDA
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

Supervised Dimensionality Reduction Method for Predicting Membrane Proteins Types

An Iterative Algorithm for KLDA Classifier

Kernel nonparametric discriminant analysis

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

A New Method for Dimensionality Reduction based on Multivariate Feature Fusion

Identification of Secretory Proteins by Separated Space Based Linear Discriminant Analysis

Folded LDA: Extending the Linear Discriminant Analysis Algorithm for Feature Extraction and Data Reduction in Hyperspectral Remote Sensing

Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition

PCA and KPCA for Predicting Membrane Protein Types

Predicting Membrane Protein Types with Dimensionality Reduction and Kernel Method

Similar Documents

Supervised Dimensionality Reduction Method for Predicting Membrane Proteins Types

An Iterative Algorithm for KLDA Classifier

Kernel nonparametric discriminant analysis

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

A New Method for Dimensionality Reduction based on Multivariate Feature Fusion

Identification of Secretory Proteins by Separated Space Based Linear Discriminant Analysis

Folded LDA: Extending the Linear Discriminant Analysis Algorithm for Feature Extraction and Data Reduction in Hyperspectral Remote Sensing

Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition

PCA and KPCA for Predicting Membrane Protein Types

Predicting Membrane Protein Types with Dimensionality Reduction and Kernel Method