NDLI: Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis

Content Provider	ACM Digital Library
Author	Zhao, Xin Cheung, Leo Wang-Kit
Abstract	Identifying significant differentially expressed genes of a disease can help understand the disease at the genomic level. A hierarchical statistical model named multiclass kernel-imbedded Gaussian process (mKIGP) is developed under a Bayesian framework for a multiclass classification problem using microarray gene expression data. Specifically, based on a multinomial probit regression setting, an empirically adaptive algorithm with a cascading structure is designed to find appropriate featuring kernels, to discover potentially significant genes, and to make optimal tumor/cancer class predictions. A Gibbs sampler is adopted as the core of the algorithm to perform Bayesian inferences. A prescreening procedure is implemented to alleviate the computational complexity. The simulated examples show that mKIGP performed very close to the Bayesian bound and outperformed the referred state-of-the-art methods in a linear case, a nonlinear case, and a case with a mislabeled training sample. Its usability has great promises to problems that linear-model-based methods become unsatisfactory. The mKIGP was also applied to four published real microarray data sets and it was very effective for identifying significant differentially expressed genes and predicting classes in all of these data sets.
Starting Page	1041
Ending Page	1053
Page Count	13
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2010.85
Volume Number	8
Issue Number	4
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gene expression, Gaussian processes, Monte Carlo methods, nonlinear multiclass systems.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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

Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis

Stable Gene Selection from Microarray Data via Sample Weighting

Pseudo-Marginal Bayesian Inference for Gaussian Processes

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

Data-Dependent Kernel Machines for Microarray Data Classification

Incorporating Nonlinear Relationships in Microarray Missing Value Imputation

Data-Dependent Kernel Machines for Microarray Data Classification

Bmc bioinformatics biomed central methodology article kernel-imbedded gaussian processes for disease classification using (2007).

Attribute clustering for grouping, selection, and classification of gene expression data

Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis

Similar Documents

Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis

Stable Gene Selection from Microarray Data via Sample Weighting

Pseudo-Marginal Bayesian Inference for Gaussian Processes

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

Data-Dependent Kernel Machines for Microarray Data Classification

Incorporating Nonlinear Relationships in Microarray Missing Value Imputation

Data-Dependent Kernel Machines for Microarray Data Classification

Bmc bioinformatics biomed central methodology article kernel-imbedded gaussian processes for disease classification using (2007).

Attribute clustering for grouping, selection, and classification of gene expression data

Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis