Computational Biology and Bioinformatics: Gene Regulation

ISBN-13: 9781138582606 / Angielski / Twarda / 2020 / 438 str.

Ka-Chun Wong (City University of Hong Ko

Opis

The advances in biotechnology such as the next generation sequencing technologies are occurring at breathtaking speed. Advances and breakthroughs give competitive advantages to those who are prepared. However, the driving force behind the positive competition is not only limited to the technological advancement, but also to the companion data analytical skills and computational methods which are collectively called computational biology and bioinformatics. Without them, the biotechnology-output data by itself is raw and perhaps meaningless. To raise such awareness, we have collected the state-of-the-art research works in computational biology and bioinformatics with a thematic focus on gene regulation in this book. This book is designed to be self-contained and comprehensive, targeting senior undergraduates and junior graduate students in the related disciplines such as bioinformatics, computational biology, biostatistics, genome science, computer science, applied data mining, applied machine learning, life science, biomedical science, and genetics. In addition, we believe that this book will serve as a useful reference for both bioinformaticians and computational biologists in the post-genomic era.

Recenzje

This dense book is a collection of peer-reviewed chapters on the subject of bioinformatics and computational biology in the context of gene regulation. Each chapter was written by a unique set of authors, and the editor compiled the totality to fill a perceived information void. Given the multitude of authors, as well as the challenging subject matter, the readability of each chapter varies. The 17 chapters are organized into six sections covering genes; RNAs; proteins; epigenetics; a case study of gene mutations linked to drug resistance; and advanced topics covering quality assurance, computational trends in sequence alignment, state estimation and process monitoring, next-generation sequencing, metabolic engineering, evolutionary conservation, and protein model ranking. All the chapters are annotated well with references, numerous figures (in color, when necessary), equations, graphs, charts, models, and tables that effectively support the text. Many tables include descriptions, remarks, additional references, and databases with website links. According to the editor, this book is intended to be "self-contained and comprehensive" and geared toward upper-level undergraduates and beginning graduate students in the interdisciplinary areas where biology, computer science, and statistics congregate.

--C. L. Iwema, University of Pittsburgh, appeard in February 2017 issues of CHOICE

Spis treści

A Survey on Computational Methods for Enhancer and Enhancer Target Predictions. Cormotif: an R Package for Jointly Detecting Differential Gene Expression in Multiple Studies. Granger Causality for Time Series Gene Expression Data. RNA Sequencing and Gene Expression Regulation. Modern Technologies and Approaches for Decoding Non-coding RNA-mediated Biological Networks in Systems Biology and Their Applications. Annotation of Hypothetical Proteins, a Functional Genomics Approach. Protein-Protein Functional Linkage Predictions: Bringing Regulation to Context. Epigenomic Analysis of Chromatin Organization and DNA Methylation. Gene Body Methylation and Transcriptional Regulation: Statistical Modelling and More. Computational Characterization of Non-small-cell Lung Cancerwith EGFR Gene Mutations and Its Application to Drug Resistance Prediction. Quality Assurance in Genome-Scale Bioinformatics Analyses. Recent Computational Trends in Biological Sequence Alignment. 13. State Estimation and Process Monitoring of Nonlinear Biological Phenomena Modeled by S-systems. Next-Generation Sequencing and Metagenomics. METABOLIC ENGINEERING- Its Dimensions and Applications. Methods to Identify Evolutionary Conserved Regulatory Elements Using Molecular Phylogenetics in Microbes. Improved Protein Model Ranking through Topological Assessment.

O autorze

Ka-Chun Wong