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10.1186/s40064-016-3252-8 http://scihub22266oqcxt.onion/10.1186/s40064-016-3252-8 C5028368!5028368!27652181     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27652181&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Springerplus 2016 ; 5 (1): ä Nephropedia Template TP {{tp|p=27652181|t=2016. An overview of topic modeling and its current applications in bioinformatics |pdf=|usr=}}{{27652181}} gab.com Text An overview of topic modeling and its current applications in bioinformatics JO: Springerplus http://www.kidney.de/pget.php?&tw=1&pmid=27652181 #FOAvip Background: With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. Description: This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Conclusion: Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers? ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research. Twit Text FOAVip An overview of topic modeling and its current applications in bioinformatics ????Springerplus http://www.kidney.de/pget.php?&tw=1&pmid=27652181 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27652181 #FOAvip English Wikipedia <ref>{{Cite pmid|27652181}}</ref> An overview of topic modeling and its current applications in bioinformatics #MMPMID27652181 Liu L; Tang L; Dong W; Yao S; Zhou W Springerplus 2016[]; 5 (1): ä PMID27652181show ga Background: With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. Description: This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Conclusion: Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers? ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research. äAn overview of topic modeling and its current applications in bioinfor(epmc).pdf DeepDyve Pubget Overpricing