Hong-Woo Chun

Publications of Hong-Woo Chun

• U-Compare bio-event meta-service: compatible BioNLP event extraction services.

  Authors: Yoshinobu Kano, Jari Bjorne, Filip Ginter, Tapio Salakoski, Ekaterina Buyko, Udo Hahn, K Bretonnel Cohen, Karin Verspoor, Christophe Roeder, Lawrence E Hunter [......] Alberto Pascual-Montano, Arzucan Ozgur, Dragomir R Radev, Sebastian Riedel, Rune Saetre, Hong-Woo Chun, Jin-Dong Kim, Sampo Pyysalo, Tomoko Ohta, Jun'ichi Tsujii

  BMC bioinformatics. 12/2011; 12(1):481.

  ABSTRACT: BACKGROUND: Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made availableABSTRACT: BACKGROUND: Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes. RESULTS: We have integrated nine event extraction systems in the U-Compare framework, making them inter-compatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. CONCLUSIONS: While individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble.

• The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications.

  Authors: Toshiaki Katayama, Mark D Wilkinson, Rutger Vos, Takeshi Kawashima, Shuichi Kawashima, Mitsuteru Nakao, Yasunori Yamamoto, Hong-Woo Chun, Atsuko Yamaguchi, Shin Kawano [......] Martin Senger, Jessica Severin, Yasumasa Shigemoto, Hideaki Sugawara, James Taylor, Oswaldo Trelles, Chisato Yamasaki, Riu Yamashita, Noriyuki Satoh, Toshihisa Takagi

  Journal of biomedical semantics. 08/2011; 2:4.

  ABSTRACT: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperabilityABSTRACT: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework.

• Bio-molecular Event Extraction with Markov Logic.

  Authors: Sebastian Riedel, Rune Sætre, Hong-Woo Chun, Toshihisa Takagi, Jun-ichi Tsujii

  Computational Intelligence. 01/2011; 27:558-582.

• Extracting Protein Sub-cellular Localizations from Literature.

  Authors: Hong-Woo Chun, Jin-Dong Kim, Yunsoo Choi, Won-Kyung Sung

  Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings; 01/2010

• Natural Language Query Processing for Life Science Knowledge - Position Paper.

  Authors: Jin-Dong Kim, Yasunori Yamamoto, Atsuko Yamaguchi, Mitsuteru Nakao, Kenta Oouchida, Hong-Woo Chun, Toshihisa Takagi

  Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings; 01/2010

• The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*.

  Authors: Toshiaki Katayama, Kazuharu Arakawa, Mitsuteru Nakao, Keiichiro Ono, Kiyoko F Aoki-Kinoshita, Yasunori Yamamoto, Atsuko Yamaguchi, Shuichi Kawashima, Hong-Woo Chun, Jan Aerts [......] Daron M Standley, Hideaki Sugawara, Toshiyuki Tashiro, Oswaldo Trelles, Rutger A Vos, Mark D Wilkinson, William York, Christian M Zmasek, Kiyoshi Asai, Toshihisa Takagi

  Journal of biomedical semantics. 01/2010; 1(1):8.

  Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands forWeb services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.

• The H-Invitational Database (H-InvDB), a comprehensive annotation resource for human genes and transcripts.

  Authors: Chisato Yamasaki, Katsuhiko Murakami, Yasuyuki Fujii, Yoshiharu Sato, Erimi Harada, Jun-ichi Takeda, Takayuki Taniya, Ryuichi Sakate, Shingo Kikugawa, Makoto Shimada [......] Yumi Yamaguchi-Kabata, Takeshi Itoh, Teruyoshi Hishiki, Satoshi Fukuchi, Ken Nishikawa, Sumio Sugano, Nobuo Nomura, Yoshio Tateno, Tadashi Imanishi, Takashi Gojobori

  Nucleic acids research. 02/2008; 36(Database issue):D793-9.

  Here we report the new features and improvements in our latest release of the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/), a comprehensive annotation resource for human genes andHere we report the new features and improvements in our latest release of the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/), a comprehensive annotation resource for human genes and transcripts. H-InvDB, originally developed as an integrated database of the human transcriptome based on extensive annotation of large sets of full-length cDNA (FLcDNA) clones, now provides annotation for 120 558 human mRNAs extracted from the International Nucleotide Sequence Databases (INSD), in addition to 54 978 human FLcDNAs, in the latest release H-InvDB_4.6. We mapped those human transcripts onto the human genome sequences (NCBI build 36.1) and determined 34 699 human gene clusters, which could define 34 057 (98.1%) protein-coding and 642 (1.9%) non-protein-coding loci; 858 (2.5%) transcribed loci overlapped with predicted pseudogenes. For all these transcripts and genes, we provide comprehensive annotation including gene structures, gene functions, alternative splicing variants, functional non-protein-coding RNAs, functional domains, predicted sub cellular localizations, metabolic pathways, predictions of protein 3D structure, mapping of SNPs and microsatellite repeat motifs, co-localization with orphan diseases, gene expression profiles, orthologous genes, protein-protein interactions (PPI) and annotation for gene families. The current H-InvDB annotation resources consist of two main views: Transcript view and Locus view and eight sub-databases: the DiseaseInfo Viewer, H-ANGEL, the Clustering Viewer, G-integra, the TOPO Viewer, Evola, the PPI view and the Gene family/group.

• Extraction of gene-disease relations from Medline using domain dictionaries and machine learning.

  Authors: Hong-Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, Jun'ichi Tsujii

  Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. 02/2006;

  We describe a system that extracts disease-gene relations from Medline. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates byWe describe a system that extracts disease-gene relations from Medline. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction is heavily dependent upon the performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.

• Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

  Authors: Hong-Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, Jun'ichi Tsujii

  BMC bioinformatics. 02/2006; 7 Suppl 3:S4.

  BACKGROUND: Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of theBACKGROUND: Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. METHODS: We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. RESULTS: Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. CONCLUSION: A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

• Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning.

  Authors: Hong-Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, Jun-ichi Tsujii

  Biocomputing 2006, Proceedings of the Pacific Symposium, Maui, Hawaii, USA, 3-7 January 2006; 01/2006

• Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

  Authors: Hong-Woo Chun, Yoshimasa Tsuruoka, Jin-Dong Kim, Rie Shiba, Naoki Nagata, Teruyoshi Hishiki, Jun-ichi Tsujii

  BMC Bioinformatics. 01/2006; 7.