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10.1186/s12859-018-2240-0 http://scihub22266oqcxt.onion/10.1186/s12859-018-2240-0 C6048865!6048865!30012087     free     free     free 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       BMC+Bioinformatics 2018 ; 19 (ä): ä Nephropedia Template TP {{tp|p=30012087|t=2018. Discovering themes in biomedical literature using a projection-based algorithm |pdf=|usr=}}{{30012087}} gab.com Text Discovering themes in biomedical literature using a projection-based algorithm JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=30012087 #FOAvip Background: The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously. Results: We present an algorithm to analyze document collections that is based on a notion of a theme, defined as a dual representation based on a set of documents and key terms. In this work, a novel vector space mechanism is proposed for computing themes. Starting with a single document, the theme algorithm treats terms and documents as explicit components, and iteratively uses each representation to refine the other until the theme is detected. The method heavily relies on an optimization routine that we refer to as the projection algorithm which, under specific conditions, is guaranteed to converge to the first singular vector of a data matrix. We apply our algorithm to a collection of about sixty thousand PubMed ? documents examining the subject of Single Nucleotide Polymorphism, evaluate the results and show the effectiveness and scalability of the proposed method. Conclusions: This study presents a contribution on theoretical and algorithmic levels, as well as demonstrates the feasibility of the method for large scale applications. The evaluation of our system on benchmark datasets demonstrates that our method compares favorably with the current state-of-the-art methods in computing clusters of documents with coherent topic terms. Electronic supplementary material: The online version of this article (10.1186/s12859-018-2240-0) contains supplementary material, which is available to authorized users. Twit Text FOAVip Discovering themes in biomedical literature using a projection-based algorithm ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=30012087 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30012087 #FOAvip English Wikipedia <ref>{{Cite pmid|30012087}}</ref> Discovering themes in biomedical literature using a projection-based algorithm #MMPMID30012087 Yeganova L; Kim S; Balasanov G; Wilbur WJ BMC Bioinformatics 2018[]; 19 (ä): ä PMID30012087show ga Background: The need to organize any large document collection in a manner that facilitates human comprehension has become crucial with the increasing volume of information available. Two common approaches to provide a broad overview of the information space are document clustering and topic modeling. Clustering aims to group documents or terms into meaningful clusters. Topic modeling, on the other hand, focuses on finding coherent keywords for describing topics appearing in a set of documents. In addition, there have been efforts for clustering documents and finding keywords simultaneously. Results: We present an algorithm to analyze document collections that is based on a notion of a theme, defined as a dual representation based on a set of documents and key terms. In this work, a novel vector space mechanism is proposed for computing themes. Starting with a single document, the theme algorithm treats terms and documents as explicit components, and iteratively uses each representation to refine the other until the theme is detected. The method heavily relies on an optimization routine that we refer to as the projection algorithm which, under specific conditions, is guaranteed to converge to the first singular vector of a data matrix. We apply our algorithm to a collection of about sixty thousand PubMed ? documents examining the subject of Single Nucleotide Polymorphism, evaluate the results and show the effectiveness and scalability of the proposed method. Conclusions: This study presents a contribution on theoretical and algorithmic levels, as well as demonstrates the feasibility of the method for large scale applications. The evaluation of our system on benchmark datasets demonstrates that our method compares favorably with the current state-of-the-art methods in computing clusters of documents with coherent topic terms. Electronic supplementary material: The online version of this article (10.1186/s12859-018-2240-0) contains supplementary material, which is available to authorized users. äDiscovering themes in biomedical literature using a projection-based a(epmc).pdf DeepDyve Pubget Overpricing