| I-Jen Chiang |
------>authors3_c=None ------>paper_class1=2 ------>Impact_Factor=None ------>paper_class3=0 ------>paper_class2=0 ------>vol= ------>confirm_bywho=jack ------>insert_bywho=ijchiang ------>Jurnal_Rank=None ------>authors4_c=None ------>comm_author=1 ------>patent_EDate=None ------>authors5_c=None ------>publish_day=None ------>paper_class2Letter=None ------>page2= ------>medlineContent= ------>unit=E0700 ------>insert_date=20030325 ------>iam=1 ------>update_date= ------>author=??? ------>change_event=5 ------>ISSN=None ------>authors_c=None ------>score=31 ------>journal_name=Taipei Data Mining Conference 2002 ------>paper_name=Text Mining ------>confirm_date=20031023 ------>tch_id=090112 ------>pmid=19908383 ------>page1= ------>fullAbstract=A critical goal of pharmacogenomics research is to identify genes that can explain variation in drug response. We have previously reported a method that creates a genome-scale ranking of genes likely to interact with a drug. The algorithm uses information about drug structure and indications of use to rank the genes. Although the algorithm has good performance, its performance depends on a curated set of drug-gene relationships that is expensive to create and difficult to maintain. In this work, we assess the utility of text mining in extracting a network of drug-gene relationships automatically. This provides a valuable aggregate source of knowledge, subsequently used as input into the algorithm that ranks potential pharmacogenes. Using a drug-gene network created from sentence-level co-occurrence in the full text of scientific articles, we compared the performance to that of a network created by manual curation of those articles. Under a wide range of conditions, we show that a knowledge base derived from text-mining the literature performs as well as, and sometimes better than, a high-quality, manually curated knowledge base. We conclude that we can use relationships mined automatically from the literature as a knowledgebase for pharmacogenomics relationships. Additionally, when relationships are missed by text mining, our system can accurately extrapolate new relationships with 77.4% precision. ------>tmu_sno=None ------>sno=6677 ------>authors2=None ------>authors3=None ------>authors4=None ------>authors5=None ------>authors6=None ------>authors6_c=None ------>authors=I-Jen Chiang ------>delete_flag=0 ------>SCI_JNo=None ------>authors2_c=None ------>publish_area=None ------>updateTitle=Improving the prediction of pharmacogenes using text-derived drug-gene relationships. ------>language=2 ------>check_flag= ------>submit_date= ------>country=None ------>no= ------>patent_SDate=None ------>update_bywho= ------>publish_year=2002 ------>submit_flag= ------>publish_month=None |