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10.1093/jamia/ocw041 http://scihub22266oqcxt.onion/10.1093/jamia/ocw041 C4926749!4926749 !27121612     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27121612 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Am+Med+Inform+Assoc 2016 ; 23 (4 ): 766-72 Nephropedia Template TP {{tp|p=27121612 |t=2016. Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature |pdf=|usr=}}{{27121612 }} gab.com Text Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=27121612 #FOAvip OBJECTIVE: Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. MATERIALS AND METHODS: We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. RESULTS: The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. DISCUSSION: To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. CONCLUSIONS: The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale. Twit Text FOAVip Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=27121612 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27121612 #FOAvip English Wikipedia <ref>{{Cite pmid|27121612 }}</ref> Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature #MMPMID27121612 Singhal A ; Simmons M ; Lu Z J Am Med Inform Assoc 2016[Jul]; 23 (4 ): 766-72 PMID27121612 show ga OBJECTIVE: Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. MATERIALS AND METHODS: We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. RESULTS: The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. DISCUSSION: To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. CONCLUSIONS: The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale. |*Machine Learning [MESH] |*Mutation [MESH] |*Precision Medicine [MESH] |Breast Neoplasms/*genetics [MESH] |Computational Biology [MESH] |Data Mining/*methods [MESH] |Databases as Topic [MESH] |Female [MESH] |Humans [MESH] |Male [MESH] |Prostatic Neoplasms/*genetics [MESH]Text mining for precision medicine: automating disease-mutation relati(epmc).pdf DeepDyve Pubget Overpricing