This thorough volume provides an in-depth introduction to and discussion of microRNAs (miRs) and their targets, miR functions, and computational techniques applied in miR research, thus serving the need for a comprehensive book focusing on miR target genes, miR regulation mechanisms, miR functions performed in various human diseases, and miR databases/knowledgebases. Without prior knowledge of the area of study, computational biologists, computer scientists, bioinformaticians, bench biologists, as well as clinical investigators will find it easy to follow the techniques in this collection. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that ensures successful results. Accessible and practical, Bioinformatics in MicroRNA Research functions as an ideal guide for researchers of all backgrounds to explore this vital area of study.

In miRNomics: MicroRNA Biology and Computational Analysis, expert researchers in the field present an overview of the current state of the art and aim to put the respective areas of research into a larger perspective. These include methods and techniques ranging from miRNA biogenesis, their biological function, computational analyses to their medical implications and applications. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, miRNomics: MicroRNA Biology and Computational Analysis seeks to aid scientists in the further study into miRNA research and statistics.

This edited reflects the current state of knowledge about the role of microRNAs in the formation and progression of solid tumours. The main focus lies on computational methods and applications, together with cutting edge experimental techniques that are used to approach all aspects of microRNA regulation in cancer. We are sure that the emergence of high-throughput quantitative techniques will make this integrative approach absolutely necessary in the near future. This book will be a resource for researchers starting out with cancer microRNA research, but is also intended for the experienced researcher who wants to incorporate concepts and tools from systems biology and bioinformatics into his work. Bioinformaticians and modellers are provided with a general perspective on microRNA biology in cancer, and the state-of-the-art in computational microRNA biology.

This book presents current advances in the emerging interdisciplinary field of microRNA research of human cancers from a unique perspective of quantitative sciences: bioinformatics, computational and systems biology, and mathematical modeling. This volume contains adaptations and critical reviews of recent state-of-the-art studies, ranging from technological advances in microRNA detection and profiling, clinically oriented microRNA profiling in several human cancers, to a systems biology analysis of global patterns of microRNA regulation of signaling and metabolic pathways. Interactions with transcription factor regulatory networks and mathematical modeling of microRNA regulation are also discussed.

MicroRNA Protocols provides diverse, novel, and useful descriptions of miRNAs in several species, including plants, worms, flies, fish, chicks, mice, and humans. These include some useful adaptations and applications that could be relevant to the wider research community who are already familiar with the identification of miRNAs. This volume will stimulate the reader to explore diverse ways to understanding the mechanism in which miRNAs facilitate the molecular aspects of the biomedical research.

Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.

Advances in high-throughput biological methods have led to the publication of a large number of genome-wide studies in human and animal models. In this context, recent tools from bioinformatics and computational biology have been fundamental for the analysis of these genomic studies. The book Bioinformatics and Human Genomics Research provides updated and comprehensive information about multiple approaches of the application of bioinformatic tools to research in human genomics. It covers strategies analysis of genome-wide association studies, genome-wide expression studies and genome-wide DNA methylation, among other topics. It provides interesting strategies for data mining in human genomics, network analysis, prediction of binding sites for miRNAs and transcription factors, among other themes. Experts from all around the world in bioinformatics and human genomics have contributed chapters in this book. Readers will find this book as quite useful for their in silico explorations, which would contribute to a better and deeper understanding of multiple biological processes and of pathophysiology of many human diseases.

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.

The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics.