Microarray Data Analysis of Yeast Data using Sparse Non-Negative Matrix Factorization

被引：0

作者：

Passi, Kalpdrum ^{[1
]}

Draper, Paul ^{[1
]}

Santala, Jillana ^{[1
]}

Jain, Chakresh Kumar ^{[2
]}

机构：

[1] Laurentian Univ, Dept Math & Comp Sci, Sudbury, ON, Canada

[2] Jaypee Inst Informat Technol, Dept Biotechnol, Noida, India

来源：

PROCEEDINGS 2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI) | 2017年

关键词：

sparse non-negative matrix factorization; yeast; microarray data; k-means; CLASS DISCOVERY; CANCER;

D O I：

10.1109/CSCI.2017.221

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Microarray expression data contains observations from thousands of genes across hundreds of samples. To extract meaningful information from these large datasets, the dimensionality reduction technique known as non-negative matrix factorization, or NMF, is introduced. This tool transforms the data and makes it more amenable to clustering. NMF was applied to a yeast microarray dataset. Three main clusters were discovered, corresponding to three distinct metabolic cycles. The data were also clustered using the k-means algorithm, and the clustering result was highly similar to that obtained by NMF.

引用

页码：1259 / 1264

页数：6

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