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A comprehensive analysis of classification algorithms for cancer prediction from gene expression

Published: 09 September 2015 Publication History

Abstract

With the advent of inexpensive microarray technology, biologists have become increasingly reliant on gene expression analysis for detecting disease states, including diagnosis of cancerous tissue [12]. While random forests and SVMs have proven to be popular methods for expression analysis, little work has been done to compare these methods with AdaBoost, a popular ensemble learning algorithm, across a wide array of cancer prediction tasks. Our work shows AdaBoost outperforms other approaches on binary predictions while random forests and SVMs are the best choice in multi-class predictions.

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  • (2021)Performance Analysis of Leukemic Gene Expression Profiles using Classification2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA52323.2021.9676059(1053-1058)Online publication date: 2-Dec-2021

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        cover image ACM Conferences
        BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics
        September 2015
        683 pages
        ISBN:9781450338530
        DOI:10.1145/2808719
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        • (2021)Performance Analysis of Leukemic Gene Expression Profiles using Classification2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA52323.2021.9676059(1053-1058)Online publication date: 2-Dec-2021

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