Cancer Gene Diagnosis of Chiaretti et al. Microarray

Shuichi Shinmura ()
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Shuichi Shinmura: Seikei University

Chapter Chapter 9 in High-dimensional Microarray Data Analysis, 2019, pp 359-392 from Springer

Abstract: Abstract This chapter introduces the cancer gene diagnosis of Chiaretti microarray that consists of 128 patients and 12,625 genes. RIP finds 128 SMs, and Revised LP-OLDF finds 124 SMs. We confirm the defect of Revised LP-OLDF, also. Because both SMs are almost the same results, we introduce only the results of 124 SMs. In Sect. 9.2, we confirm the 7,626 correlations of 124 LpDSs are greater than 0.359 and standard statistical methods cannot find the linear separable facts of SMs. Thus, we conclude three signal data made by RIP, Revised LP-OLDF, and H-SVM are the better definition of the signal instead of SMs. Also, we explain how to build 124 LpDSs. In Sect. 9.3, the 124 SMs are evaluated by RatioSVs of six MP-based LDFs and NMs of statistical discriminant functions. In Sect. 9.4, five hierarchical cluster methods analyze three signal data of 124 RipDSs, LpDSs, and HsvmDSs. In Sects. 9.5 and 9.6, PCA analyzes signal data and transposed signal data. Section 9.7 concludes six microarrays have almost the same results. We believe that the consistency of these results confirms the reliability of cancer gene diagnosis.

Keywords: Chiaretti microarray; Cancer gene diagnosis; Malignancy indexes; Small Matryoshka (SM); RIP discriminant scores (RipDSs); Signal data made by RipDSs; Hierarchical clustering; Principal component analysis (PCA) (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_9

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DOI: 10.1007/978-981-13-5998-9_9

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