NDLI: On Efficient Feature Ranking Methods for High-Throughput Data Analysis

Content Provider	IEEE Xplore Digital Library
Author	Bo Liao Yan Jiang Wei Liang Lihong Peng Li Peng Hanyurwimfura, D. Zejun Li Min Chen
Copyright Year	2004
Abstract	Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology-related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are incorporated in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework. Thus, the interactions between features are considered and the optimal feature subset can be guaranteed. In addition, this study presents a theoretical justification. Empirical experiments demonstrate the effectiveness and efficiency of the two algorithms in comparison with some state-of-the-art feature ranking methods through a set of real-world gene expression data sets.
Sponsorship	IEEE Computer Society
Starting Page	1374
Ending Page	1384
Page Count	11
File Size	726924
File Format	PDF
ISSN	15455963
Volume Number	12
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information analysis Bioinformatics Computational biology Data mining Regression analysis Regression Feature ranking microarray data analysis, manifold learning ℓ2,1-norm microarray data analysis convex optimization regression
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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

A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

Data analytics and clinical feature ranking of medical records of patients with sepsis

High Confidence Rule Mining for Microarray Analysis

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data

Feature Subset Selection and Ranking for Data Dimensionality Reduction

Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity

Gene Classification Using Parameter-Free Semi-Supervised Manifold Learning

msBiodat analysis tool, big data analysis for high-throughput experiments

On Efficient Feature Ranking Methods for High-Throughput Data Analysis

Similar Documents

A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis

Data analytics and clinical feature ranking of medical records of patients with sepsis

High Confidence Rule Mining for Microarray Analysis

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data

Feature Subset Selection and Ranking for Data Dimensionality Reduction

Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity

Gene Classification Using Parameter-Free Semi-Supervised Manifold Learning

msBiodat analysis tool, big data analysis for high-throughput experiments

On Efficient Feature Ranking Methods for High-Throughput Data Analysis