A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …

This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

The Bayesian network is one of the most important architectures for representing and
reasoning with multivariate probability distributions. When used in conjunction with …

This invaluable textbook presents a self-contained introduction to the field of bioinformatics.
Providing a comprehensive breadth of coverage while remaining accessibly concise, the …

An introduction to machine learning methods and their applications to problems in
bioinformatics Machine learning techniques are increasingly being used to address …

Abstract Machine learning (ML) deals with the automated learning of machines without
being programmed explicitly. It focuses on performing data-based predictions and has …

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for
researchers and students in statistics, machine learning, and the biological sciences. The …

The interdisciplinary nature of bioinformatics presents a research challenge in integrating
concepts, methods, software and multiplatform data. Although there have been rapid …

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The term machine learning refers to a set of topics dealing with the creation and evaluation
of algorithms that facilitate pattern recognition, classification, and prediction, based on …