Cancer Gene Diagnosis of Singh et al. Microarray

Shuichi Shinmura ()
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Shuichi Shinmura: Seikei University

Chapter Chapter 7 in High-dimensional Microarray Data Analysis, 2019, pp 291-327 from Springer

Abstract: Abstract Chapter 1 explained the new theory of discriminant analysis after R. A. Fisher (Theory). The theory solved five problems completely. Especially, Revised IP-OLDF (RIP) and Method2 firstly succeeded in the cancer gene analysis. RIP could find six microarrays were LSD (Fact3). LINGO Program3 of Method2 could decompose the microarray into many SMs and another noise subspace (Fact4). In Chap. 2 , we make signal data made by RIP discriminant scores (RipDSs). Our breakthrough opens the new frontier of cancer gene diagnosis and malignancy indexes. We find the new problem (Problem6): “Why could no researchers find the linear separable facts in microarrays and SM from 1970?” In this book, we explain the several answers of Problem6. In this chapter, we survey how to make different RipDSs from many SMs. It explains why microarray consists of many SMs and the different RipDSs. By these results, we wish to classify SMs into several categories of malignancy indexes in the future.

Keywords: Singh’s microarray; Cancer gene diagnosis; Malignancy indexes and RatioSV; RIP discriminant scores (RipDSs); Signal data made by RipDSs; Correlations of RipDSs (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-5998-9_7

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DOI: 10.1007/978-981-13-5998-9_7

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