Machine Learning in Bioinformatics

Jyotsna Talreja Wassan; Haiying / HY Wang; Huiru Zheng

doi:10.1016/B978-0-12-809633-8.20331-2

Machine Learning in Bioinformatics

Jyotsna Talreja Wassan
, Haiying / HY Wang
, Huiru Zheng

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Citations (Scopus)

Abstract

Machine Learning (ML) is an adaptive process of interpreting information from real-world data sets, by making use of computers that learn from experiences. Bioinformatics is emerging as an interdisciplinary science of interpreting biological data using such adaptive computational procedures. This article provides an overview on the ML tools and techniques, which are useful in bioinformatics and computational biology.

Original language	English
Title of host publication	Encyclopedia of Bioinformatics and Computational Biology
Editors	Shoba Ranganathan, Michael Gribskov, Kenta Nakai, Christian Schönbach
Publisher	Elsevier
Pages	300-308
Number of pages	9
Volume	1
ISBN (Electronic)	9780128114148
ISBN (Print)	9780128114322
DOIs	https://doi.org/10.1016/B978-0-12-809633-8.20331-2
Publication status	Published (in print/issue) - 6 Sept 2018

Keywords

Classification
Clustering
Deep Learning
reinforcement learning
Semi-supervised learning
supervised learning
Unsupervised Learning

Access to Document

10.1016/B978-0-12-809633-8.20331-2

Integrative data analysis for the prediction of metagenomic functions
Wassan, J. T. (Author), Zheng, H. (Supervisor), Wang, H. (Supervisor) & Browne, F. (Supervisor), Mar 2020
Student thesis: Doctoral Thesis

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Cite this

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title = "Machine Learning in Bioinformatics",

abstract = "Machine Learning (ML) is an adaptive process of interpreting information from real-world data sets, by making use of computers that learn from experiences. Bioinformatics is emerging as an interdisciplinary science of interpreting biological data using such adaptive computational procedures. This article provides an overview on the ML tools and techniques, which are useful in bioinformatics and computational biology. ",

keywords = "Classification, Clustering, Deep Learning, reinforcement learning, Semi-supervised learning, supervised learning, Unsupervised Learning",

author = "Wassan, \{Jyotsna Talreja\} and Wang, \{Haiying / HY\} and Huiru Zheng",

year = "2018",

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TY - CHAP

T1 - Machine Learning in Bioinformatics

AU - Wassan, Jyotsna Talreja

AU - Wang, Haiying / HY

AU - Zheng, Huiru

PY - 2018/9/6

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N2 - Machine Learning (ML) is an adaptive process of interpreting information from real-world data sets, by making use of computers that learn from experiences. Bioinformatics is emerging as an interdisciplinary science of interpreting biological data using such adaptive computational procedures. This article provides an overview on the ML tools and techniques, which are useful in bioinformatics and computational biology.

AB - Machine Learning (ML) is an adaptive process of interpreting information from real-world data sets, by making use of computers that learn from experiences. Bioinformatics is emerging as an interdisciplinary science of interpreting biological data using such adaptive computational procedures. This article provides an overview on the ML tools and techniques, which are useful in bioinformatics and computational biology.

KW - Classification

KW - Clustering

KW - Deep Learning

KW - reinforcement learning

KW - Semi-supervised learning

KW - supervised learning

KW - Unsupervised Learning

U2 - 10.1016/B978-0-12-809633-8.20331-2

DO - 10.1016/B978-0-12-809633-8.20331-2

M3 - Chapter

SN - 9780128114322

VL - 1

SP - 300

EP - 308

BT - Encyclopedia of Bioinformatics and Computational Biology

A2 - Ranganathan, Shoba

A2 - Gribskov, Michael

A2 - Nakai, Kenta

A2 - Schönbach, Christian

PB - Elsevier

ER -

Machine Learning in Bioinformatics

Abstract

Keywords

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Student theses

Integrative data analysis for the prediction of metagenomic functions

Cite this