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10.1186/1753-6561-7-S7-S2 http://scihub22266oqcxt.onion/10.1186/1753-6561-7-S7-S2 C4042834!4042834!24564884     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Proc 2013 ; 7 (Suppl 7): S2 Nephropedia Template TP {{tp|p=24564884|t=2013. Conceptualization of molecular findings by mining gene annotations |pdf=|usr=}}{{24564884}} gab.com Text Conceptualization of molecular findings by mining gene annotations JO: BMC Proc http://www.kidney.de/pget.php?&tw=1&pmid=24564884 #FOAvip Background: The Gene Ontology (GO) is an ontology representing molecular biology concepts related to genes and their products. Current annotations from the GO Consortium tend to be highly specific, and contemporary genome-scale studies often return a long list of genes of potential interest, such as genes in a cancer tumor that are differentially expressed than those found in normal tissue. It is therefore a challenging task to reveal, at a conceptual level, the major functional themes in which genes are involved. Presently, there is a need for tools capable of revealing such themes through mining and representing semantic information in an objective and quantitative manner. Methods: In this study, we utilized the hierarchical organization of the GO to derive a more abstract representation of the major biological processes of a list of genes based on their annotations. We cast the task as follows: given a list of genes, identify non-disjoint, functionally coherent subsets, such that the functions of the genes in a subset are summarized by an informative GO term that accurately captures the semantic information of the original annotations. Results: We evaluated different metrics for assessing information loss when merging GO terms, and different statistical schemes to assess the functional coherence of a set of genes. We found that the best discriminative power was achieved by using a combination of the information-content-based measure as the information-loss metric, and the graph-based statistics derived from a Steiner tree connecting genes in an augmented GO graph. Conclusions: Our methods provide an objective and quantitative approach to capturing the major directions of gene functions in a context-specific fashion. Twit Text FOAVip Conceptualization of molecular findings by mining gene annotations ????BMC Proc http://www.kidney.de/pget.php?&tw=1&pmid=24564884 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24564884 #FOAvip English Wikipedia <ref>{{Cite pmid|24564884}}</ref> Conceptualization of molecular findings by mining gene annotations #MMPMID24564884 Chen V; Lu X BMC Proc 2013[]; 7 (Suppl 7): S2 PMID24564884show ga Background: The Gene Ontology (GO) is an ontology representing molecular biology concepts related to genes and their products. Current annotations from the GO Consortium tend to be highly specific, and contemporary genome-scale studies often return a long list of genes of potential interest, such as genes in a cancer tumor that are differentially expressed than those found in normal tissue. It is therefore a challenging task to reveal, at a conceptual level, the major functional themes in which genes are involved. Presently, there is a need for tools capable of revealing such themes through mining and representing semantic information in an objective and quantitative manner. Methods: In this study, we utilized the hierarchical organization of the GO to derive a more abstract representation of the major biological processes of a list of genes based on their annotations. We cast the task as follows: given a list of genes, identify non-disjoint, functionally coherent subsets, such that the functions of the genes in a subset are summarized by an informative GO term that accurately captures the semantic information of the original annotations. Results: We evaluated different metrics for assessing information loss when merging GO terms, and different statistical schemes to assess the functional coherence of a set of genes. We found that the best discriminative power was achieved by using a combination of the information-content-based measure as the information-loss metric, and the graph-based statistics derived from a Steiner tree connecting genes in an augmented GO graph. Conclusions: Our methods provide an objective and quantitative approach to capturing the major directions of gene functions in a context-specific fashion. äConceptualization of molecular findings by mining gene annotations (epmc).pdf DeepDyve Pubget Overpricing