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  • 1
    Electronic Resource
    Electronic Resource
    Structural basis of the α 1 –β subunit interaction of voltage-gated Ca 2+ channels (2004)
    [s.l.] : Macmillian Magazines Ltd.
    Nature 429 (2004), S. 675-680 
    Details
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] High-voltage-activated Ca2+ channels are essential for diverse biological processes. They are composed of four or five subunits, including α1, α2-δ, β and γ (ref. 1). Their expression and function are critically ...
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1038/nature02641
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    Paper (German National Licenses)
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  • 2
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    Genes, Vol. 8, Pages 392: Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective (2017)
    Molecular Diversity Preservation International (MDPI)
    In: Genes
    Details
    Publication Date: 2017-12-19
    Description: Genes, Vol. 8, Pages 392: Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective Genes doi: 10.3390/genes8120392 Authors: Lei Chen Yu-Hang Zhang JiaRui Li ShaoPeng Wang YunHua Zhang Tao Huang Yu-Dong Cai The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein–protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity–disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity–disease co-occurrence and provide useful insights for disease prevention and intervention.
    Electronic ISSN: 2073-4425
    Topics: Biology
    Published by Molecular Diversity Preservation International (MDPI)
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  • 3
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    Genes, Vol. 9, Pages 449: A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes (2018)
    Molecular Diversity Preservation International (MDPI)
    In: Genes
    Details
    Publication Date: 2018-09-08
    Description: Genes, Vol. 9, Pages 449: A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes Genes doi: 10.3390/genes9090449 Authors: JiaRui Li Lei Chen Yu-Hang Zhang XiangYin Kong Tao Huang Yu-Dong Cai Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function.
    Electronic ISSN: 2073-4425
    Topics: Biology
    Published by Molecular Diversity Preservation International (MDPI)
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