Cancer Gene Diagnosis of Golub et al. Microarray

Shuichi Shinmura ()
Additional contact information
Shuichi Shinmura: Seikei University

Chapter Chapter 5 in High-dimensional Microarray Data Analysis, 2019, pp 191-235 from Springer

Abstract: Abstract Golub microarray consists of 72 patients and 7,129 genes. They analyzed the microarray by various statistical methods. For example, they analyzed “marker” genes having the highest correlation with the target class-by-class separation statistics (signal-to-noise ratio), weighted votes, and SOM. Mainly, discriminant analysis is the most proper method to identify oncogenes. However, because the statistical discriminant analysis was useless at all, medical researchers had developed many methods. Our theory shows that six microarrays are LSD (MNM = 0). Method2 can decompose the microarray into many Small Matryoshka (SM) those are LSD. Then, by analyzing SM, we achieved cancer gene diagnosis by malignancy indexes. If Golub et al. validate our results, cancer gene diagnosis will be more improved. Method2 already obtained the different sets of SM in Chap. 2 . In 2018, we change the number of iterations of RIP and Revised LP-OLDF in Method2 and decided the proper number of iterations as same as Alon's microarray in Chap. 4 . We obtained SM by those iteration numbers. We examined the signal data made by RIP discriminant scores (RipDSs). We confirm the Revised LP-OLDF cannot find all SMs as same as Alon's microarray. Thus, we analyze only 179 SMs obtained by the RIP and examine the correlation coefficient of 179 RipDSs. We compare RatioSV of six MP-based LDFs and NM of statistical discriminant function. Then, the cluster analysis and PCA analyze signal data made by RIP and H-SVM. We propose the possibility of cancer gene diagnosis such as malignancy indexes. We propose how to find new subclasses of cancer pointed out by Golub et al. (Science 286(5439): 531–537, 1999).

Keywords: Golub microarray; Cancer gene diagnosis; Malignancy indicators; Small Matryoshka (SM); RatioSV; RipDSs; LpDSs; HsvmDSs; Signal data (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-5998-9_5

Ordering information: This item can be ordered from
http://www.springer.com/9789811359989

DOI: 10.1007/978-981-13-5998-9_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().