00679nas a2200181 4500008004100000245012900041210006900170260000900239300001400248490000700262653000800269100001500277700001500292700001200307700002200319700001900341856013700360 2007 eng d 00 aLarge-scale regulatory network analysis from microarray data: Modified Bayesian network learning and association rule mining 0 aLargescale regulatory network analysis from microarray data Modi c2007 a1207-1225 0 v43 10 aBIS 1 aHuang, Zan 1 aLi, Jiexun 1 aSu, Hua 1 aWatts, George, S. 1 aChen, Hsinchun u/biblio/large-scale-regulatory-network-analysis-microarray-data-modified-bayesian-network-learning-0 00594nas a2200169 4500008004100000245008400041210006900125260000900194300001200203490000700215653000800222100001500230700001200245700001900257700002600276856012200302 2007 eng d 00 aOptimal search-based gene subset selection for gene array cancer classification 0 aOptimal searchbased gene subset selection for gene array cancer c2007 a398-405 0 v11 10 aBIS 1 aLi, Jiexun 1 aSu, Hua 1 aChen, Hsinchun 1 aFutscher, Bernard, W. u/biblio/optimal-search-based-gene-subset-selection-gene-array-cancer-classification-0 02033nas a2200205 4500008004100000245011700041210006900158260000900227300001200236490000700248520135500255653001501610653000801625100002501633700001201658700002001670700001901690700001901709856009901728 2007 eng d 00 aUser-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System 0 aUserCentered Evaluation of Arizona BioPathway An Information Ext c2007 a527-536 0 v11 3 aExplosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This article presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature viewing method. Relation aggregation significantly contributes to knowledge acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multi-view, relation-based interface which supports user-controlled exploration of pathway information across multiple granularities. 10 aAccounting 10 aBIS 1 aQuiñones, Karin, D. 1 aSu, Hua 1 aMarshall, Byron 1 aEggers, Shauna 1 aChen, Hsinchun uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4300830&arnumber=4300844&count=17&index=5 01468nas a2200205 4500008004100000245006900041210006900110260000900179300001300188490000700201520086100208653001501069653000801084100002001092700001201112700002101124700001901145700001901164856007901183 2006 eng d 00 aAggregating Automatically Extracted Regulatory Pathway Relations 0 aAggregating Automatically Extracted Regulatory Pathway Relations c2006 a100- 108 0 v10 3 aAutomatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations. 10 aAccounting 10 aBIS 1 aMarshall, Byron 1 aSu, Hua 1 aMcDonald, Daniel 1 aEggers, Shauna 1 aChen, Hsinchun uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_IEEE_TITB_2005.pdf 00575nas a2200145 4500008004100000245009800041210006900139260002700208653000800235100001500243700001200258700001200270700001900282856012800301 2006 eng d 00 aA Bayesian framework of integrating gene functional relations from heterogeneous data sources 0 aBayesian framework of integrating gene functional relations from aPhoenix, AZ, USA c2006 10 aBIS 1 aLi, Jiexun 1 aLi, Xin 1 aSu, Hua 1 aChen, Hsinchun u/biblio/bayesian-framework-integrating-gene-functional-relations-heterogeneous-data-sources 00673nas a2200181 4500008004100000245012800041210006900169260000900238300001400247490000700261653000800268100001500276700001200291700001200303700001900315700002500334856013200359 2006 eng d 00 aA framework of integrating gene functional relations from heterogeneous data sources: An experiment on Arabidopsis thaliana 0 aframework of integrating gene functional relations from heteroge c2006 a2037-2043 0 v22 10 aBIS 1 aLi, Jiexun 1 aLi, Xin 1 aSu, Hua 1 aChen, Hsinchun 1 aGalbraith, David, W. u/biblio/framework-integrating-gene-functional-relations-heterogeneous-data-sources-experiment-0 00566nas a2200145 4500008004100000245008400041210006900125260002700194653000800221100001500229700001200244700001900256700002500275856012000300 2006 eng d 00 aOptimal search-based gene subset selection for microarray cancer classification 0 aOptimal searchbased gene subset selection for microarray cancer aPhoenix, AZ, USA c2006 10 aBIS 1 aLi, Jiexun 1 aSu, Hua 1 aChen, Hsinchun 1 aFutscher, Bernard, W u/biblio/optimal-search-based-gene-subset-selection-microarray-cancer-classification 01720nas a2200169 4500008004100000245010700041210006900148260000900217520116000226653001501386653000801401100002001409700001201429700001801441700001901459856007201478 2005 eng d 00 aLinking Ontological Resources Using Aggregatable Substance Identifiers to Organize Extracted Relations 0 aLinking Ontological Resources Using Aggregatable Substance Ident c2005 3 aSystems that extract biological regulatory pathway relations from free-text sources are
intended to help researchers leverage vast and growing collections of research literature.
Several systems to extract such relations have been developed but little work has focused on
how those relations can be usefully organized (aggregated) to support visualization systems or
analysis algorithms. Ontological resources that enumerate name strings for different types of
biomedical objects should play a key role in the organization process. In this paper we
delineate five potentially useful levels of relational granularity and propose the use of
aggregatable substance identifiers to help reduce lexical ambiguity. An aggregatable
substance identifier applies to a gene and its products. We merged 4 extensive lexicons and
compared the extracted strings to the text of five million MEDLINE abstracts. We report on
the ambiguity within and between name strings and common English words. Our results show
an 89% reduction in ambiguity for the extracted human substance name strings when using an
aggregatable substance approach. 10 aAccounting 10 aBIS 1 aMarshall, Byron 1 aSu, Hua 1 aMcDonald, Dan 1 aChen, Hsinchun uhttp://people.oregonstate.edu/~marshaby/Papers/marshall_PSB2005.pdf 00468nas a2200133 4500008004100000245006100041210006100102260002000163653000800183100001500191700001200206700001900218856009700237 2005 eng d 00 aOptimal search based gene selection for cancer prognosis 0 aOptimal search based gene selection for cancer prognosis aOmaha, NE c2005 10 aBIS 1 aLi, Jiexun 1 aSu, Hua 1 aChen, Hsinchun u/biblio/optimal-search-based-gene-selection-cancer-prognosis 01188nas a2200181 4500008004100000245005600041210005600097260000900153520064400162653001500806653000800821100002000829700002100849700001200870700001900882700001900901856008600920 2005 eng d 00 aVisualizing Aggregated Biological Pathway Relations 0 aVisualizing Aggregated Biological Pathway Relations c2005 3 aThe Genescene development team has constructed an aggregation interface for automatically-extracted biomedical pathway
relations that is intended to help researchers identify and process relevant information from the vast digital library of abstracts found in the National Library of Medicine’s PubMed collection.
Users view extracted relations at various levels of relational granularity in an interactive and visual node-link interface. Anecdotal feedback reported here suggests that this multigranular visual paradigm aligns well with various research tasks,
helping users find relevant articles and discover new information. 10 aAccounting 10 aBIS 1 aMarshall, Byron 1 aQuiñones, Karin 1 aSu, Hua 1 aEggers, Shauna 1 aChen, Hsinchun uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_JCDL_2005_Aggregation.pdf 01663nas a2200193 4500008004100000245009100041210006900132260000900201300001100210490000700221520107000228653001501298653000801313100001801321700001901339700001201358700002001370856007901390 2004 eng d 00 aExtracting Gene Pathway Relations Using a Hybrid Grammar: The Arizona Relation Parser 0 aExtracting Gene Pathway Relations Using a Hybrid Grammar The Ari c2004 a3370-8 0 v20 3 aMotivation: Text-mining research in the biomedical domain has been motivated by the rapid growth of new research findings. Improving the accessibility of findings has potential to speed hypothesis generation.Results: We present the Arizona Relation Parser that differs from other parsers in its use of a broad coverage syntax-semantic hybrid grammar. While syntax grammars have generally been tested over more documents, semantic grammars have outperformed them in precision and recall. We combined access to syntax and semantic information from a single grammar. The parser was trained using 40 PubMed abstracts and then tested using 100 unseen abstracts, half for precision and half for recall. Expert evaluation showed that the parser extracted biologically relevant relations with 89% precision. Recall of expert identified relations with semantic filtering was 35 and 61% before semantic filtering. Such results approach the higher-performing semantic parsers. However, the AZ parser was tested over a greater variety of writing styles and semantic content.
10 aAccounting 10 aBIS 1 aMcDonald, Dan 1 aChen, Hsinchun 1 aSu, Hua 1 aMarshall, Byron uhttp://people.oregonstate.edu/~marshaby/Papers/MCDONALD_BIOINFORMATICS.pdf