{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T15:12:00Z","timestamp":1751555520191,"version":"3.41.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2015,7,4]],"date-time":"2015-07-04T00:00:00Z","timestamp":1435968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"JSPS Grant-in-Aid for Scientific Research","award":["26280038"],"award-info":[{"award-number":["26280038"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2015,8,13]]},"abstract":"<jats:p>\n            With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies (\n            <jats:italic>GWAS<\/jats:italic>\n            ) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph (\n            <jats:italic>PGBG<\/jats:italic>\n            ) with a gene-disease bipartite graph (\n            <jats:italic>GDBG<\/jats:italic>\n            ) by producing a phenotype-disease bipartite graph (\n            <jats:italic>PDBG<\/jats:italic>\n            ), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight (\n            <jats:italic>BIW<\/jats:italic>\n            ) prediction method to\n            <jats:italic>PDBG<\/jats:italic>\n            for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain (\n            <jats:italic>NDCG<\/jats:italic>\n            ), Mean Average Precision (\n            <jats:italic>MAP<\/jats:italic>\n            ), and Kendall\u2019s tau metrics. Further experiments are conducted with well-known\n            <jats:italic>TF \u00b7 IDF<\/jats:italic>\n            ,\n            <jats:italic>BM25<\/jats:italic>\n            , and\n            <jats:italic>Jenson-Shannon divergence<\/jats:italic>\n            as baselines. The result shows that our proposed method outperforms the known related tool\n            <jats:italic>Phenomizer<\/jats:italic>\n            in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than\n            <jats:italic>Phenomizer<\/jats:italic>\n            in terms of Kendall\u2019s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods.\n          <\/jats:p>","DOI":"10.1145\/2700487","type":"journal-article","created":{"date-parts":[[2015,7,6]],"date-time":"2015-07-06T14:04:16Z","timestamp":1436191456000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs"],"prefix":"10.1145","volume":"6","author":[{"given":"Md Zia","family":"Ullah","sequence":"first","affiliation":[{"name":"Toyohashi University of Technology"}]},{"given":"Masaki","family":"Aono","sequence":"additional","affiliation":[{"name":"Toyohashi University of Technology"}]},{"given":"Md Hanif","family":"Seddiqui","sequence":"additional","affiliation":[{"name":"University of Chittagong"}]}],"member":"320","published-online":{"date-parts":[[2015,7,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/75556"},{"key":"e_1_2_1_2_1","volume-title":"an information site on rare diseases. Soins","author":"Aym\u00e9 S","year":"2003","unstructured":"S Aym\u00e9 . 2003. Orphanet , an information site on rare diseases. Soins ; la revue de r\u00e9f\u00e9rence infirmi\u00e8re 672 ( 2003 ), 46--47. S Aym\u00e9. 2003. Orphanet, an information site on rare diseases. Soins; la revue de r\u00e9f\u00e9rence infirmi\u00e8re 672 (2003), 46--47."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3109\/13816818909083773"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2918"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2012-13-6-r46"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508037.2508044"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq538"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1136\/adc.2009.171827"},{"key":"e_1_2_1_9_1","volume-title":"Revisiting the foundations of network analysis. Science 325, 5939","author":"Butts C. 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Hamosh, A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. McKusick. 2005. Online mendelian inheritance in man (OMIM), a knowledge base of human genes and genetic disorders. Nucleic Acids Research 33, suppl 1 (2005), D514.","journal-title":"Nucleic Acids Research"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra0808700"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3163\/1536-5050.97.1.009"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg1521"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/345508.345545"},{"key":"e_1_2_1_26_1","volume-title":"Lobach","author":"Kawamoto Kensaku","year":"2005","unstructured":"Kensaku Kawamoto , Caitlin A. Houlihan , E. Andrew Balas , and David F . Lobach . 2005 . Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ 330, 7494 (2005), 765. Kensaku Kawamoto, Caitlin A. Houlihan, E. Andrew Balas, and David F. Lobach. 2005. Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ 330, 7494 (2005), 765."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/30.1-2.81"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/33.3.239"},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"M. Khoury S. Bedrosian M. Gwinn J. Higgins J. Ioannidis and J. Little. 2009. Human Genome Epidemiology 2nd Edition: Building the Evidence for Using Genetic Information to Improve Health and Prevent Disease. Oxford University Press USA. http:\/\/books.google.co.jp\/books?id&equals;qZWwO5TchD0C.  M. Khoury S. Bedrosian M. Gwinn J. Higgins J. Ioannidis and J. Little. 2009. Human Genome Epidemiology 2nd Edition: Building the Evidence for Using Genetic Information to Improve Health and Prevent Disease. 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