Using Gene-level to Generalize Transcript-level Classification Performance on Multiple Colorectal Cancer Microarray Studies

Hendrick Gao Min Lim, Yuan Chii Gladys Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Several classification algorithms have been applied into microarray studies for colorectal cancer identification. Algorithms such as naïve bayes, random forest, logistic regression, support vector machine, and deep learning have been successfully used in previous studies. The accuracy of these algorithms shown promising result through n-fold validation. However, most of studies are limited to transcript-level that will implicate to biased interpretation of classification result due to different microarray platform entanglement. Therefore, we applied gene-level classification to generalize transcript-level classification result on multiple colorectal cancer microarray studies through different classification algorithms including: naïve Bayes, random forest, logistic regression, support vector machine, and deep learning. We evaluated classification performance using several parameters including: accuracy, area under ROC curve, recall and precision. As the result, we found biased classification result in transcript-level from multiple microarray studies can be solved through gene-level classification by applying annotation and merging. In addition, applying batch effect removal method can make gene-level classification performance slightly improved. Furthermore, annotation and merging also can be used to solve another biased result of feature selection in transcript-level.

Original languageEnglish
Title of host publicationICBBB 2020 - Proceedings of 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics
PublisherAssociation for Computing Machinery, Inc
Pages64-68
Number of pages5
ISBN (Electronic)9781450376761
DOIs
Publication statusPublished - Jan 19 2020
Event10th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2020 - Kyoto, Japan
Duration: Jan 19 2020Jan 22 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2020
CountryJapan
CityKyoto
Period1/19/201/22/20

Keywords

  • Classification
  • Colorectal cancer
  • Gene-level
  • Microarray
  • Transcript-level

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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