Extraction of Genes and Transcripts Associated with Liver Cancer Using Machine Learning

Koshiro Sekine; Teruhisa Hochin; Hiroki Nomiya; Hideki Yoshida

doi:10.52731/ijscai.v6.i1.628

Koshiro Sekine Kyoto Institute of Technology
Teruhisa Hochin Kyoto Institute of Technology
Hiroki Nomiya Kyoto Institute of Technology
Hideki Yoshida Kyoto Institute of Technology

DOI: https://doi.org/10.52731/ijscai.v6.i1.628

Keywords: Differential Gene Expression, Differential Transcript Expression, Feature Extraction, Machine Learning

Abstract

The rapid development of large-scale genome analysis technology in recent years has facilitated the acquisition of genome data, but it is difficult to extract effective information from a large amount of data. To solve this problem, machine learning has been attracting attention. In this paper, we used machine learning to extract genes and transcripts associated with liver cancer. Liver cancer is difficult to cure completely and is generally treated by surgery, making it difficult to treat elderly people or those with reduced physical strength. As an overview of the method, using the liver cancer dataset of NBDC, genes and transcripts extracted by using statistical hypothesis tests were used as input to the machine learning to create a classifier. Then, genes and transcripts with high contribution rate to the classification were extracted from the classifier. As a result, we obtained genes and transcripts that were considered to be associated with liver cancer from the created classifier. The results of this paper are expected to contribute to the development of gene therapy for liver cancer. In addition, since the method in this paper is not specialized for liver cancer, it can be expected to be applied to other cancers.

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