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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Gene Selection Based on Consistency Modelling, Algorithms and Applications - Genetic Algorithm Application in Bioinformatics Data Analysis

Gene Selection Based on Consistency Modelling, Algorithms and Applications - Genetic Algorithm Application in Bioinformatics Data Analysis

Hu, Yingjie

Paperback

General Science

ISBN10: 3639008839
ISBN13: 9783639008838
Publisher: Blues Kids Of Amer
Published: Apr 29 2008
Pages: 112
Weight: 0.35
Height: 0.23 Width: 6.00 Depth: 9.00
Language: English
Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis. The proposed consistency concept has been investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy.

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