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    The hormonal control of sexual development (1981)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 1981-03-20
    Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Wilson, J D -- George, F W -- Griffin, J E -- AM03892/AM/NIADDK NIH HHS/ -- New York, N.Y. -- Science. 1981 Mar 20;211(4488):1278-84.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/7010602" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Anti-Mullerian Hormone ; Estradiol/metabolism/*physiology ; Female ; *Glycoproteins ; Gonadotropins/physiology ; *Growth Inhibitors ; Humans ; Male ; Morphogenesis ; Mullerian Ducts ; Ovary/embryology ; Rabbits ; Receptors, Androgen/metabolism ; *Sex Differentiation ; Testicular Hormones/*physiology ; Testis/embryology/secretion ; Testosterone/metabolism/*physiology ; Time Factors ; Urogenital System/embryology ; Wolffian Ducts
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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    Bayesian correlated clustering to integrate multiple datasets (2012)
    Oxford University Press
    Details
    Publication Date: 2012-12-08
    Description: Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/ . Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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