%0 Book Section %1 statphys23_0632 %A Goh, K.I. %A Cusick, M.E. %A Valle, D. %A Childs, B. %A Vidal, M. %A Barabási, A.L. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K biological complex disease human networks statphys23 topic-11 %T Human diseasome: A complex network approach of human diseases %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=632 %X Understanding of disease mechanisms is an important goal in human systems biology. Towards this goal, we construct and study the ``human diseasome,'' that summarizes all known disorder-gene associations to provide a global systems-level relationship between all human genetic disorders and genes implicated therein, offering a platform to integrate other human genomic and proteomic informations into single graph-theoretic framework. The obtained human diseasome suggests that diseases are modular: disorders of similar type aggregates reflecting the common genetic origin of many human disorders, and genes implicated in similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, suggesting the presence of disease-specific functional modules. We present the applications of the diseasome concept such as to the gene centrality assessment, disease gene prediction, and the drug-target network, and discuss the prospect of its extension.

@incollection{statphys23_0632, abstract = {Understanding of disease mechanisms is an important goal in human systems biology. Towards this goal, we construct and study the ``human diseasome,'' that summarizes all known disorder-gene associations to provide a global systems-level relationship between all human genetic disorders and genes implicated therein, offering a platform to integrate other human genomic and proteomic informations into single graph-theoretic framework. The obtained human diseasome suggests that diseases are modular: disorders of similar type aggregates reflecting the common genetic origin of many human disorders, and genes implicated in similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, suggesting the presence of disease-specific functional modules. We present the applications of the diseasome concept such as to the gene centrality assessment, disease gene prediction, and the drug-target network, and discuss the prospect of its extension.}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Goh, K.I. and Cusick, M.E. and Valle, D. and Childs, B. and Vidal, M. and Barab\'asi, A.L.}, biburl = {https://www.bibsonomy.org/bibtex/24c572ccbe8479bc254209d6de31c814e/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {2174308d5956965d6ba753362eda996c}, intrahash = {4c572ccbe8479bc254209d6de31c814e}, keywords = {biological complex disease human networks statphys23 topic-11}, month = {9-13 July}, timestamp = {2007-06-20T10:16:25.000+0200}, title = {Human diseasome: A complex network approach of human diseases}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=632}, year = 2007 }