Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.

ABSTRACT:

Background

Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor - the laterality - can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality samples.

Results

A data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes (left, right, bilateral). The aim of this work is to understand the gen

SUBMITTER: Hamzeh O 

PROVIDER: S-EPMC7068980 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature