NDLI: The study of ovarian carcinoma gene regulatory mechanism based on the shortest path algorithm and conditional probabilities

Content Provider	IEEE Xplore Digital Library
Author	Jui-Ming Chen Meng-Hsiun Tsai Shih-Huei Wang Sheng-Hsiung Chiu
Copyright Year	2011
Description	Author affiliation: Institute of Genomics and Bioinformatics, National Chung Hsing University Taiwan, R.O.C (Shih-Huei Wang) \|\| Department of Endocrinology and Metabolism, Tungs' Taichung MetroHarbor Hospital Taiwan, R.O.C (Jui-Ming Chen) \|\| Troilus Biotechnology Co., Ltd, Taiwan (Sheng-Hsiung Chiu) \|\| Department of Management Information Systems, National Chung Hsing University Taiwan, R.O.C (Meng-Hsiun Tsai)
Abstract	In the current cancer research field, microarray is one of the most commonly used tools. It has the advantage of containing a large amount of data, which helped us in recording gene expressions in cancer and comparing the difference between normal cells and cancer cells. However, contemporary cancer research does not have a positive definition in how to analyze the microarray data. In this essay, we utilize the microarray data from the carcinoma cancer as primary sample. And we apply statistical methods and mathematical calculations to establish a diseases analyzing model. At first, we use principal component analysis to process the pre-selected data. Then we use ANOVA to select the genes with significant expression differences to be our target genes. Finally we use supervised learning method to evaluate the accuracy of classification. This analytical model helps to reduce the number of incorrect attempts and cut down the time which has to be spent in experiments. This model can analyze complicated cancer expression data, and it can also be useful in researches for other diseases.
Starting Page	1638
Ending Page	1642
File Size	432390
Page Count	5
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6019914
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene regulator network Accuracy Analysis of Variance Supervised learning Gene expression Decision trees Analysis of variance Cancer Principal component analysis Tumors
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

Genetic regulatory networks established by shortest path algorithm and conditional probabilities for ovarian carcinoma microarray data

Possible Disease-Link Genetic Pathways Constructed by Hierarchical Clustering and Conditional Probabilities of Ovarian Carcinoma Microarray Data

Supervised iterative learning algorithm for eigenspace models

Aiding Classification of Gene Expression Data with Feature Selection : A Comparative Study

Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks

Modeling Gene Regulatory Subnetworks from Time Course Gene Expression Data

Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS (2008)

Comparison analysis on supervised learning based solutions for sports video categorization

Learning regulatory programs that accurately predict differential expression with MEDUSA

The study of ovarian carcinoma gene regulatory mechanism based on the shortest path algorithm and conditional probabilities

Similar Documents

Genetic regulatory networks established by shortest path algorithm and conditional probabilities for ovarian carcinoma microarray data

Possible Disease-Link Genetic Pathways Constructed by Hierarchical Clustering and Conditional Probabilities of Ovarian Carcinoma Microarray Data

Supervised iterative learning algorithm for eigenspace models

Aiding Classification of Gene Expression Data with Feature Selection : A Comparative Study

Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks

Modeling Gene Regulatory Subnetworks from Time Course Gene Expression Data

Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS (2008)

Comparison analysis on supervised learning based solutions for sports video categorization

Learning regulatory programs that accurately predict differential expression with MEDUSA

The study of ovarian carcinoma gene regulatory mechanism based on the shortest path algorithm and conditional probabilities