TY - JOUR T1 - Large-scale regulatory network analysis from microarray data: Modified Bayesian network learning and association rule mining JF - Decision Support Systems Y1 - 2007 A1 - Huang,Zan A1 - Li,Jiexun A1 - Su,Hua A1 - Watts,George S. A1 - Chen,Hsinchun KW - BIS VL - 43 U2 - a U4 - 86818299904 ID - 86818299904 ER - TY - JOUR T1 - Optimal search-based gene subset selection for gene array cancer classification JF - IEEE Transactions on Information Technology in Biomedicine Y1 - 2007 A1 - Li,Jiexun A1 - Su,Hua A1 - Chen,Hsinchun A1 - Futscher,Bernard W. KW - BIS VL - 11 CP - 4 U2 - a U4 - 86818201600 ID - 86818201600 ER - TY - JOUR T1 - User-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System JF - IEEE Transactions on Information Technology in Biomedicine Y1 - 2007 A1 - Quiñones,Karin D. A1 - Su,Hua A1 - Marshall,Byron A1 - Eggers,Shauna A1 - Chen,Hsinchun KW - Accounting KW - BIS AB - Explosive 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. VL - 11 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4300830&arnumber=4300844&count=17&index=5 CP - 5 U2 - a U4 - 648212480 ID - 648212480 ER - TY - JOUR T1 - Aggregating Automatically Extracted Regulatory Pathway Relations JF - IEEE Transactions on Information Technology in Biomedicine Y1 - 2006 A1 - Marshall,Byron A1 - Su,Hua A1 - McDonald,Daniel A1 - Eggers,Shauna A1 - Chen,Hsinchun KW - Accounting KW - BIS AB - Automatic 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. VL - 10 UR - http://people.oregonstate.edu/~marshaby/Papers/Marshall_IEEE_TITB_2005.pdf CP - 1 U2 - a U4 - 648208384 ID - 648208384 ER - TY - HEAR T1 - A Bayesian framework of integrating gene functional relations from heterogeneous data sources Y1 - 2006 A1 - Li,Jiexun A1 - Li,Xin A1 - Su,Hua A1 - Chen,Hsinchun KW - BIS JA - American Medical Informatics Association (AMIA) Spring Congress CY - Phoenix, AZ, USA U2 - c U4 - 98584031232 ID - 98584031232 ER - TY - JOUR T1 - A framework of integrating gene functional relations from heterogeneous data sources: An experiment on Arabidopsis thaliana JF - Bioinformatics Y1 - 2006 A1 - Li,Jiexun A1 - Li,Xin A1 - Su,Hua A1 - Chen,Hsinchun A1 - Galbraith,David W. KW - BIS VL - 22 CP - 16 U2 - a U4 - 86818365440 ID - 86818365440 ER - TY - HEAR T1 - Optimal search-based gene subset selection for microarray cancer classification Y1 - 2006 A1 - Li,Jiexun A1 - Su,Hua A1 - Chen,Hsinchun A1 - Futscher,Bernard W KW - BIS JA - American Medical Informatics Association (AMIA) Spring Congress CY - Phoenix, AZ, USA U2 - c U4 - 98584055808 ID - 98584055808 ER - TY - CONF T1 - Linking Ontological Resources Using Aggregatable Substance Identifiers to Organize Extracted Relations T2 - Proceedings of the Pacific Symposium on Biocomputing, Jan 4-8, 2005, Big Island, Hawaii Y1 - 2005 A1 - Marshall,Byron A1 - Su,Hua A1 - McDonald,Dan A1 - Chen,Hsinchun KW - Accounting KW - BIS AB - Systems 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. JA - Proceedings of the Pacific Symposium on Biocomputing, Jan 4-8, 2005, Big Island, Hawaii UR - http://people.oregonstate.edu/~marshaby/Papers/marshall_PSB2005.pdf U2 - b U4 - 2606753793 ID - 2606753793 ER - TY - HEAR T1 - Optimal search based gene selection for cancer prognosis Y1 - 2005 A1 - Li,Jiexun A1 - Su,Hua A1 - Chen,Hsinchun KW - BIS JA - Americas Conference on Information Systems (AMCIS’05) CY - Omaha, NE U2 - c U4 - 98584080384 ID - 98584080384 ER - TY - CONF T1 - Visualizing Aggregated Biological Pathway Relations T2 - Proceedings of the 2005 Joint ACM/IEEE Conference on Digital Libraries (JCDL 2005), June 7-11, 2005 , Denver, CO Y1 - 2005 A1 - Marshall,Byron A1 - Quiñones,Karin A1 - Su,Hua A1 - Eggers,Shauna A1 - Chen,Hsinchun KW - Accounting KW - BIS AB - The 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. JA - Proceedings of the 2005 Joint ACM/IEEE Conference on Digital Libraries (JCDL 2005), June 7-11, 2005 , Denver, CO UR - http://people.oregonstate.edu/~marshaby/Papers/Marshall_JCDL_2005_Aggregation.pdf U2 - b U4 - 2606727169 ID - 2606727169 ER - TY - JOUR T1 - Extracting Gene Pathway Relations Using a Hybrid Grammar: The Arizona Relation Parser JF - Bioinformatics Y1 - 2004 A1 - McDonald,Dan A1 - Chen,Hsinchun A1 - Su,Hua A1 - Marshall,Byron KW - Accounting KW - BIS AB - Motivation: 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. VL - 20 UR - http://people.oregonstate.edu/~marshaby/Papers/MCDONALD_BIOINFORMATICS.pdf CP - 18 U2 - a U4 - 648206336 ID - 648206336 ER -