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生物数据挖掘中机器学习技术的当前发展

Current Developments in Machine Learning Techniques in Biological Data Mining.

作者信息

Dumancas Gerard G, Adrianto Indra, Bello Ghalib, Dozmorov Mikhail

机构信息

Department of Mathematics and Physical Sciences, Louisiana State University, Alexandria, LA, USA.

Quantitative Analysis Core, Arthritis & Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.

出版信息

Bioinform Biol Insights. 2017 Mar 22;11:1177932216687545. doi: 10.1177/1177932216687545. eCollection 2017.

Abstract

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

摘要

本增刊旨在聚焦于使用机器学习技术来生成有关生物数据的有意义信息。本增刊旨在为在这个快速发展的领域工作的科学家和研究人员提供由该领域顶尖国际专家撰写的在线开放获取文章。生物学领域的进展创造了大量机会,得以实施现代计算和统计技术。特别是机器学习方法,作为计算机科学的一个子领域,已发展成为应用于广泛生物信息学应用的不可或缺的工具。因此,它被广泛用于研究导致特定疾病的潜在机制以及生物标志物发现过程。随着这一特定科学领域的发展,需要获取最新的高质量学术文章,这些文章将利用科学家和研究人员在机器学习技术挖掘生物数据的各种应用中的知识。

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