Biological Pattern Discovery with R: Machine Learning Approaches

This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.

Contents:
Preface
Introduction
Responsive Gene Discovery
Protease Cleavage Pattern Discovery
Genetic-Epigenetic Interplay Discovery
Spectral Pattern Discovery
Gene Expression Pattern Discovery
Whole Genome Pattern Discovery
Optimised Peptide Pattern Discovery
Advanced Subjects
References
Index

Readership: Junior bioinformaticians and computational biologists, postgraduate students.