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Release from the Curse of High Dimensional Data Analysis

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Big Data, Cloud Computing, and Data Science Engineering (BCD 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 844))

Abstract

Golub et al. started their research to find oncogenes and new cancer subclasses from microarray around 1970. They opened their microarray on the Internet. The other five medical projects published their papers and released their microarrays, also. However, because Japanese cancer specialist advised us that NIH decided that these researches were useless after 2004, we guess medical groups abandoned these researches. Although we are looking for NIH’s report, we cannot find it now. Meanwhile, many researchers of statistics, machine learning and bioengineering continue to research as a new theme of high-dimensional data analysis using microarrays. However, they could not succeed in cancer gene analysis as same as medical researches (Problem5). We discriminated six microarrays by Revised IP-OLDF (RIP) and solved Problem5 within 54 days until December 20, 2015. We obtained the two surprising results. First, MNMs of six microarrays are zero (Fact3). Second, RIP could decompose microarray into many linearly separable gene subspaces (SMs) and noise subspace (Fact4). These two new facts indicate that we are free from the curse of high dimensional microarray data and complete the cancer gene analysis. Because all SMs are LSD and small samples, we thought to analysis all SMs by statistical methods and obtained useful results. However, we were disappointed that statistical methods do not show linearly separable facts and are useless for cancer gene diagnosis (Problem6). After trial and error, we make signal data made by RIP discriminant scores (RipDSs) from SM. Through this breakthrough, we find useful information by correlation analysis, cluster analysis, and PCA in addition to RIP, Revised LP-OLDF and hard margin SVM (H-SVM). We think that the discovery of the above two new facts is the essence of Problem5. Moreover, we claim to solve Prpblem6 and obtain useful medical care information from signal data as a cancer gene diagnosis. However, our claim needs validation by medical specialists. In this research, we introduce the reason why no researchers could succeed in the cancer gene diagnosis by microarrays from 1970.

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Authors and Affiliations

  1. Emeritus Seikei University, 1-8-7-301 Sakasai Kashiwa City, Chiba, 277-0042, Japan

    Shuichi Shinmura

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  1. Shuichi Shinmura
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Correspondence to Shuichi Shinmura .

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Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mount Pleasant, MI, USA

    Roger Lee

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Shinmura, S. (2020). Release from the Curse of High Dimensional Data Analysis. In: Lee, R. (eds) Big Data, Cloud Computing, and Data Science Engineering. BCD 2019. Studies in Computational Intelligence, vol 844. Springer, Cham. https://doi.org/10.1007/978-3-030-24405-7_12

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