FNMF-ITWC Algorithm Applied to the Cancer Gene Expression Data

Duo Wang

doi:10.4028/www.scientific.net/AMM.678.12

Paper Titles

Preface and Conference Organization

A New Random Encryption Algorithm for Character Type Cipher
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Exploiting Thread-Level Parallelism Based on Balancing Load for Speculative Multithreading
p.8

FNMF-ITWC Algorithm Applied to the Cancer Gene Expression Data
p.12

Fuzzy Set Based Clustering Algorithm of Web Text
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MIMU Parameter Identification for Large Misalignment Angles Based on AFSA
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Reduction of Risk Control Indicators of ERP System Based on Rough Set
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Research on Stereo Matching Algorithm in Intelligent Vehicle Application
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Researching on the Single Lock Chamber Automatic Layout Algorithm
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 678FNMF-ITWC Algorithm Applied to the Cancer Gene...

FNMF-ITWC Algorithm Applied to the Cancer Gene Expression Data

Abstract:

The clustering analysis of the cancer gene expression data can provide bases for the early diagnosis of cancer and accurate classification of the cancer subtypes. For the characteristics of cancer gene expression data, an algorithm named FNMF-ITWC (Fast Nonnegative Matrix Factorization Interrelated Two_Way Clustering) is proposed. FNMF-ITWC algorithm firstly selects genes from the original gene expression data, implements non-negative matrix factorization on the row (gene dimension), and then performs clustering on the column (sample dimension). Matlab experimental results show that FNMF-ITWC algorithm improves the computing speed of the algorithm and reduces the data storage space. At the same time, it is able to reveal correlation among genes under certain experimental conditions and the correlation among experiments for some genes.

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Periodical:

Applied Mechanics and Materials (Volume 678)

Pages:

12-18

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.678.12

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Online since:

October 2014

Authors:

Duo Wang*

Keywords:

Biclustering Algorithm, Cancer Gene Expression Data, Data Dimensionality Reduction, Iterative Clustering, Non-Negative Matrix Factorization

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* - Corresponding Author

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