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A Shape Descriptor for Fast Complementarity Matching in Molecular Docking

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Classification of GPCRs Using Family Specific Motifs

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Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra

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Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis

Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression

Molecular Pattern Discovery Based on Penalized Matrix Decomposition

Nonparametric Clustering for Studying RNA Conformations

On Lattice Protein Structure Prediction Revisited

Recipe for Uncovering Predictive Genes Using Support Vector Machines Based on Model Population Analysis

Selecting Oligonucleotide Probes for Whole-Genome Tiling Arrays with a Cross-Hybridization Potential

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Co-evolution Is Incompatible with the Markov Assumption in Phylogenetics

Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression

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From Gene Trees to Species Trees II: Species Tree Inference by Minimizing Deep Coalescence Events

Haplotype Inference Constrained by Plausible Haplotype Data

Identification of Relevant Properties for Epitopes Detection Using a Regression Model

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Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra

Content Provider	ACM Digital Library
Author	Oh, Jung Hun Gao, Jean
Abstract	The classification of serum samples based on mass spectrometry (MS) has been increasingly used for monitoring disease progression and for diagnosing early disease. However, the classification task in mass spectrometry data is extremely challenging due to the very huge size of peaks (features) on mass spectra. Linear discriminant analysis (LDA) has been widely used for dimension reduction and feature extraction in many applications. However, the conversional LDA suffers from the singularity problem when dealing with high-dimensional features. Another critical limitation is its linearity property which results in failing in classification problems over nonlinearly clustered data sets. To overcome such problems, we develop a new fast kernel discriminant analysis (FKDA) that is pretty fast in the calculation of optimal discriminant vectors. FKDA is applied to the classification of liver cancer mass spectrometry data that consist of three categories: hepatocellular carcinoma, cirrhosis, and healthy that was originally analyzed by Ressom et al. [CHECK END OF SENTENCE]. We demonstrate the superiority and effectiveness of FKDA when compared to other classification techniques.
Starting Page	1522
Ending Page	1534
Page Count	13
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2010.42
Volume Number	8
Issue Number	6
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-11-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	FKDA, LDA, hepatocellular carcinoma, cirrhosis, classification, singularity.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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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
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Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra

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