Series: Chapman & Hall/CRC Mathematical and Computational Biology Hardcover: 294 pages Publisher: Chapman and Hall/CRC (December 22, 2015) Language: English ISBN-10: 1498724523 ISBN-13: 978-1498724524 Demystifies Biomedical and Biological Big Data Analyses Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances your computational and quantitative skills so that you can exploit the Big Data being generated in the current omics era. The book explores many significant topics of Big Data analyses in an easily understandable format. It describes popular tools and software for Big Data analyses and explains next-generation DNA sequencing data analyses. It also discusses comprehensive Big Data analyses of several major areas, including the integration of omics data, pharmacogenomics, electronic health record data, and drug discovery. Accessible to biologists, biomedical scientists, bioinformaticians, and computer data analysts, the book keeps complex mathematical deductions and jargon to a minimum. Each chapter includes a theoretical introduction, example applications, data analysis principles, step-by-step tutorials, and authoritative references
2型糖尿病发病是环境因素与遗传因素相互作用的结果，已被鉴定与其相关的基因超过400个，这些基因也与肥胖、脂代谢紊乱和心血管病等密切相关。然而，目前检测出来的许多基因突变是低频率、罕见的变异，它们对总体疾病发生风险的作用尚未完全阐明，也缺乏相对应的靶向药物。 本研究主要是在前期研究基础上，期望能通过对个体基因组、蛋白质组、代谢组和临床表型的信息等进行多组学数据深度挖掘 (OWAS-DM)，利用GWAS关联的孟德尔遗传-蛋白组量化性状基因座 (MR-pQTL)，MR-mQTL (代谢组)，MR-eQTL (表型组) 共定位分析及生物信息学分析等多组学分析策略，发现2型糖尿病发生发展的潜在机制及标志物。并利用cMAP及分子对接等人工智能虚拟筛选技术结合基于实验的亲和质谱、表面等离子共振及高内涵等筛选技术，发展针对2型糖尿病靶标的高通量药物筛选技术，为糖尿病患者的个体化精准诊断和治疗提供新的手段。翻译成英文
精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.
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