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However, the quantitative analysis of modularity in the heterogeneous network and its influence on disease-gene discovery are still unaddressed. Furthermore, the genetic correspondence of the disease subtypes can be identified by marking the genes and phenotypes in the phenotype-gene network. We present a novel network inference method to measure the network modularity, and in particular to suggest the subtypes of diseases based on the heterogeneous network.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Based on a measure which is introduced to evaluate the closeness between two nodes in the phenotype-gene heterogeneous network, we developed a Hitting-Time-based method, CIPHER-HIT, for assessing the modularity of disease gene predictions and credibly prioritizing disease-causing genes, and then identifying the genetic modules corresponding to potential subtypes of the queried phenotype. The CIPHER-HIT is free to rely on any preset parameters. We found that when taking into account the modularity levels, the CIPHER-HIT method can significantly improve the performance of disease gene predictions, which demonstrates modularity is one of the key features for credible inference of disease genes on the phenotype-gene heterogeneous network. By applying the CIPHER-HIT to the subtype analysis of Breast cancer, we found that the prioritized genes can be divided into two sub-modules, one contains the members of the Fanconi anemia gene family, and the other contains a reported protein complex MRE11\/RAD50\/NBN.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The phenotype-gene heterogeneous network contains abundant information for not only disease genes discovery but also disease subtypes detection. The CIPHER-HIT method presented here is effective for network inference, particularly on credible prediction of disease genes and the subtype analysis of diseases, for example Breast cancer. This method provides a promising way to analyze heterogeneous biological networks, both globally and locally.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-5-79","type":"journal-article","created":{"date-parts":[[2011,5,21]],"date-time":"2011-05-21T06:19:06Z","timestamp":1305958746000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network"],"prefix":"10.1186","volume":"5","author":[{"given":"Xin","family":"Yao","sequence":"first","affiliation":[]},{"given":"Han","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Yanda","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shao","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,5,20]]},"reference":[{"key":"698_CR1","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/nbt1295","volume":"25","author":"K Lage","year":"2007","unstructured":"Lage K, Karlberg EO, St\u00f8rling ZM, Olason PI, Pedersen AG, Rigina O, Hinsby AM, T\u00fcmer Z, Pociot F, Tommerup N, Moreau Y, Brunak S: A human phenome-interactome network of protein complexes implicated in genetic disorders. 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