BMC Bioinformatics 2012 11;13 Suppl 15:S7. Epub 2012 Sep 11.
Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA.
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BMC Bioinformatics 2011 Oct 18;12 Suppl 10:S4. Epub 2011 Oct 18.
School of Informatics, Indiana University, Indianapolis, IN 46202, USA.
Background: Each organ has a specific function in the body. "Organ-specificity" refers to differential expressions of the same gene across different organs. An organ-specific gene/protein is defined as a gene/protein whose expression is significantly elevated in a specific human organ. Read More
BMC Bioinformatics 2009 Oct 8;10 Suppl 11:S5. Epub 2009 Oct 8.
Indiana University School of Informatics, Indianapolis, IN 46202, USA.
Background: Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities. Read More
BMC Syst Biol 2013 Jul 23;7:64. Epub 2013 Jul 23.
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Background: Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment. Read More
BMC Bioinformatics 2011 Apr 15;12:99. Epub 2011 Apr 15.
Department of Computer Science, University of California, Irvine, USA.
Background: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. Read More