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: PrefaceIntroductionResponsive Gene DiscoveryProtease Cleavage Pattern DiscoveryGenetic-Epigenetic Interplay DiscoverySpectral Pattern DiscoveryGene Expression Pattern DiscoveryWhole Genome Pattern DiscoveryOptimised Peptide Pattern DiscoveryAdvanced SubjectsReferencesIndexReadership: Junior bioinformaticians and computational biologists, postgraduate students.
Biology Pattern Discovery;Machine Learning;Bioinformatics;R Language;Computational Biology;Biological Sequence;Genome Pattern;Gene Expression Pattern;Peptide Pattern;Gene Discovery;Pattern Discovery0Key Features:Aims to integrate the theory of machine learning with R programmingHelps readers transfer their learning of machine learning theory to practical implementationFocuses on individual biology pattern discoveries using machine learning approachesWill be useful to those who have recently started their career in bioinformatics
