Medical Biostatistics for Complex Diseases
Frank Emmert-Streib, Matthias Dehmer
John Wiley & Sons, Mar 30, 2010 - Medical - 400 pages
A collection of highly valuable statistical and computational approaches designed for developing powerful methods to analyze large-scale high-throughput data derived from studies of complex diseases. Such diseases include cancer and cardiovascular disease, and constitute the major health challenges in industrialized countries. They are characterized by the systems properties of gene networks and their interrelations, instead of individual genes, whose malfunctioning manifests in pathological phenotypes, thus making the analysis of the resulting large data sets particularly challenging. This is why novel approaches are needed to tackle this problem efficiently on a systems level. Written by computational biologists and biostatisticians, this book is an invaluable resource for a large number of researchers working on basic but also applied aspects of biomedical data analysis emphasizing the pathway level.
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Part Two Statistical and Computational Analysis Methods
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accuracy algorithm analysis apoptosis approach Bayesian networks Bioinformatics Biol biological breast cancer c-Myc cancer cells cancer stem cells carcinoma cell cycle cellular chromosome classiﬁcation clustering coefﬁcient coexpression colorectal cancer computed correlation covariance deﬁned DEGs denotes dependence disease distribution error rate estimated expression levels expression proﬁles false discovery false discovery rate ﬁeld Figure ﬁnd ﬁrst ﬁve function gene expression gene expression data gene regulatory networks genetic genomic Granger causality graph groups Hotelling’s T2 identiﬁed inference inﬂuence interactions joint core kernel matrix LASD leukemia lymphoma MANOVA method microarray data microarray datasets molecular multiple testing multivariate mutations normal null hypothesis number of genes overexpression p-values parameters patients permutation phenotype PPI networks predictive gene sets prior procedure protein receptor regulatory networks samples score selected signaling pathway signiﬁcant gene speciﬁc Stat studies survival target protein test statistics tissues tumor variables vector