High-dimensional Microarray Data Analysis : Cancer Gene Diagnosis and Malignancy Indexes by Microarray. 1st ed. 2019

種類:

電子ブック

責任表示:

by Shuichi Shinmura

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2019

著者名:

• Shinmura, Shuichi.
• SpringerLink (Online service)

ISBN:

9789811359989 [9811359989]    

注記:

1 New Theory of Discriminant Analysis and Cancer Gene Analysis -- 2 Overview of Cancer Gene Diagnosis by RIP and Revised LP-OLDF -- 3 Cancer Gene Diagnosis of Alon Microarray -- 4 Further Examinations of SMs---Defect of Revised LP-OLDF and Correlations of Genes -- 5　Cancer Gene Diagnosis of Golub et al. Microarray -- 6 Cancer Gene Diagnosis of Shipp et al. Microarray -- 7 Cancer Gene Diagnosis of Singh et al. Microarray -- 8 Cancer Gene Diagnosis of Tian et al. Microarray -- 9 Cancer Gene Diagnosis of Chiaretti et al. Microarray -- 10 LINGO Programs of Cancer Gene Analysis -- Index.
This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant

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