Gautam B. Singh – författare

The advances in genomic technologies, such as microarrays and high throughput sequencing,have expanded the realm of possibilities for capturing data and analyzing it using automatedcomputer driven bioinformatics tools. With the completion of the sequencing of genomes ofhuman and several model organisms, a quest for scientific discoveries being fueled byintegrative and multidimensional techniques in mathematics and computational sciences. Inthis volume, leading researchers and experts have provided an overview of significantconcepts from biological, mathematical, and computational perspectives. It provides a high level view of fungal genomic data integration and annotation, classification of proteins and identification of vaccine targets, identification of secretome or secreted proteins in fungal genomes, as well as tools for analyzing microarray expressionprofiles. Provides a survey of theoretical underpinnings on the technological tools and applications Discusses the tools utilized for the annotation of fungal genomes and addresses issues related to automated annotation generation in a high throughput biotechnology environment Describes the applications of the concepts and methodologies presented throughout the book

This book comprehensively covers all the core bioinformatics topics and includes practical examples completed using the MATLAB bioinformatics and machine learning toolboxes™. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics to enable physical science students to appreciate the challenges in biological data management, sequence analysis, and systems biology. The book is divided into five parts. The first one includes a survey of existing biological databases and tools that have become essential in today’s biotechnology research. The second part covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The third part of the book focuses on modeling biological sequences and patterns as Markov chains, covering core principles for analyzing and searching for sequences of significant motifs and biomarkers and developing stochastic ergodic hidden Markov models for biological sequence families. The fourth one is dedicated to systems biology and covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In turn, the last part of the book includes an introduction to machine-learning algorithms for bioinformatics and outlines strategies for developing intelligent diagnostic machine-learning applications, RNA sequence data, and deep learning systems for mass spectrometry data. All in all, this book offers a unique hands-on reference guide to bioinformatics and computational biology. This second edition has been updated to cover additional and most recent databases, and machine learning and deep learning applications in RNA sequence and mass-spectrometry data analysis. Moreover, it  presents significant enhancements to the chapter dedicated to microarray analysis, and more practical examples, with additional end-of-chapter problems.

This book comprehensively covers all the core bioinformatics topics and includes practical examples completed using the MATLAB bioinformatics and machine learning toolboxes™. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics to enable physical science students to appreciate the challenges in biological data management, sequence analysis, and systems biology. The book is divided into five parts. The first one includes a survey of existing biological databases and tools that have become essential in today’s biotechnology research. The second part covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The third part of the book focuses on modeling biological sequences and patterns as Markov chains, covering core principles for analyzing and searching for sequences of significant motifs and biomarkers and developing stochastic ergodic hidden Markov models for biological sequence families. The fourth one is dedicated to systems biology and covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In turn, the last part of the book includes an introduction to machine-learning algorithms for bioinformatics and outlines strategies for developing intelligent diagnostic machine-learning applications, RNA sequence data, and deep learning systems for mass spectrometry data. All in all, this book offers a unique hands-on reference guide to bioinformatics and computational biology. This second edition has been updated to cover additional and most recent databases, and machine learning and deep learning applications in RNA sequence and mass-spectrometry data analysis. Moreover, it  presents significant enhancements to the chapter dedicated to microarray analysis, and more practical examples, with additional end-of-chapter problems.