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The proposed algorithm is based on transfer learning and can exploit local information effectively. Each assay is automatically weighted through learning and the weights can be adaptively different in each local part. The authors\u2019 algorithm was favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network. A statistical test confirmed that the weight that our algorithm assigned to each assay was meaningful.  <\/p>","DOI":"10.4018\/jkdb.2010100205","type":"journal-article","created":{"date-parts":[[2010,4,19]],"date-time":"2010-04-19T12:16:24Z","timestamp":1271679384000},"page":"66-80","source":"Crossref","is-referenced-by-count":4,"title":["A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference"],"prefix":"10.4018","volume":"1","author":[{"given":"Tsuyoshi","family":"Kato","sequence":"first","affiliation":[{"name":"AIST Computational Biology Research Center, Japan, and Ochanomizu University, Japan"}]},{"given":"Kinya","family":"Okada","sequence":"additional","affiliation":[{"name":"KO Institute for Medical Bioinformatics, Japan"}]},{"given":"Hisashi","family":"Kashima","sequence":"additional","affiliation":[{"name":"IBM Research, Japan"}]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"Tokyo Institute of Technology, Japan"}]}],"member":"2432","reference":[{"issue":"3","key":"jkdb.2010100205-0","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool.","volume":"215","author":"S. 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