The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
At a glance
This book provides a general introduction to machine learning that is suitable for undergraduate or graduate students.
Machine Learning (McGraw-Hill,
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This book provides an overview of machine learning that is suitable for students with a strong background in statistics.
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This textbook describes kernel methods, including a detailed mathematical treatment that is suitable for quantitatively inclined graduate students.
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This textbook on probability models for machine learning is suitable for undergraduates or graduate students.
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