Translational Biomedical Informatics

This book introduces readers to essential methods and applications in translational biomedical informatics, which include biomedical big data, cloud computing and algorithms for understanding omics data, imaging data, electronic health records and public health data. The storage, retrieval, mining and knowledge discovery of biomedical big data will be among the key challenges for future translational research. The paradigm for precision medicine and healthcare needs to integratively analyze not only the data at the same level - e.g. different omics data at the molecular level - but also data from different levels - the molecular, cellular, tissue, clinical and public health level. This book discusses the following major aspects: the structure of cross-level data; clinical patient information and its shareability; and standardization and privacy. It offers a valuable guide for all biologists, biomedical informaticians and clinicians with an interest in Precision Medicine Informatics.

Autorentext

Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University and is also Director of Taicang Center for Translational Bioinformatics.

Dr. Haixu Tang is Professor at School of Informatics and Computing, Indiana University, Bloomington,USA Dr. Xiaoqian Jiang is Assistant Professor at Department of Biomedical Informatics University of California, San Diego, USA

Inhalt

NGS for sequence variants (HVP).- RNA Bioinformatics for Precision Medicine.- Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.- Computational methods in mass spectrometry based proteomics.- Informatics for Metabolomics .- Metagenomics and Single-cell Omics Data Analysis for Human Microbiome Research.- Text Mining for Precision Medicine: Bringing structure to EHRs and biomedical literature to understand genes and health.- Medical Imaging Informatics.- LIMS and clinical data management.- Biobanks and their clinical application and informatics challenges.- Methods to Improve Distributed Analysis in Biomedical Informatics.- XML, ontologies, and their clinical applications.- Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.- Network-based Biomedical Data Analysis.