Yasunori Yamamoto

Publications of Yasunori Yamamoto

• 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.

• Discriminative Application of String Similarity Methods to Chemical and Non-chemical Names for Biomedical Abbreviation Clustering.

  Authors: Atsuko Yamaguchi, Yasunori Yamamoto, Jin-Dong Kim, Toshihisa Takagi, Akinori Yonezawa

  IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, USA, 12-15 November, 2011; 01/2011

• Allie: a database and a search service of abbreviations and long forms.

  Authors: Yasunori Yamamoto, Atsuko Yamaguchi, Hidemasa Bono, Toshihisa Takagi

  Database : the journal of biological databases and curation. 01/2011; 2011:bar013.

  Many abbreviations are used in the literature especially in the life sciences, and polysemous abbreviations appear frequently, making it difficult to read and understand scientific papers that areMany abbreviations are used in the literature especially in the life sciences, and polysemous abbreviations appear frequently, making it difficult to read and understand scientific papers that are outside of a reader's expertise. Thus, we have developed Allie, a database and a search service of abbreviations and their long forms (a.k.a. full forms or definitions). Allie searches for abbreviations and their corresponding long forms in a database that we have generated based on all titles and abstracts in MEDLINE. When a user query matches an abbreviation, Allie returns all potential long forms of the query along with their bibliographic data (i.e. title and publication year). In addition, for each candidate, co-occurring abbreviations and a research field in which it frequently appears in the MEDLINE data are displayed. This function helps users learn about the context in which an abbreviation appears. To deal with synonymous long forms, we use a dictionary called GENA that contains domain-specific terms such as gene, protein or disease names along with their synonymic information. Conceptually identical domain-specific terms are regarded as one term, and then conceptually identical abbreviation-long form pairs are grouped taking into account their appearance in MEDLINE. To keep up with new abbreviations that are continuously introduced, Allie has an automatic update system. In addition, the database of abbreviations and their long forms with their corresponding PubMed IDs is constructed and updated weekly. Database URL: The Allie service is available at http://allie.dbcls.jp/.

• Figure classification in biomedical literature to elucidate disease mechanisms, based on pathways.

  Authors: Natsu Ishii, Asako Koike, Yasunori Yamamoto, Toshihisa Takagi

  Artificial intelligence in medicine. 07/2010; 49(3):135-43.

  As more full-text biomedical papers are becoming available in digitized form online, there is a need for tools to mine information from all parts of such papers. Because the figures andAs more full-text biomedical papers are becoming available in digitized form online, there is a need for tools to mine information from all parts of such papers. Because the figures and legends/captions in biomedical papers provide important information about research outcomes, mining techniques targeting them have attracted a great deal of attention. In this study, we focused on pathway figures that illustrate signaling or metabolic pathways, because many of these are important in understanding disease mechanism(s). We developed a figure classification system based on textual information contained in biomedical papers to provide an automated acquisition system for such pathway figures. We used full-text journal articles available on PubMed Central as our data set. We used several supervised machine learning methods, such as decision tree and a support vector machine, to classify figures in the data set. We compared the classification performance among the cases using only figure legends, using only sentences referring to the figure in the main text of the article, and combining figure legends with sentences referring to the figure in the main text of the article. Compared with previous related work, a sufficiently high performance was achieved with the figure legends alone. The performance with the sentences referring to the figure in the main text was actually lower than that with the figure legends alone, indicating that focusing on the main text alone is inadequate. The combination of legend and main text clearly had an effect, but including the prior and following sentences in addition to the sentence referring to the figure dramatically improved the performance. We developed an automatic pathway figure classification system based on both figure legends and the main text that has quite a high degree of accuracy. To our knowledge, this is the first attempt to address a figure classification task using legends and the main text, and it may provide a first stage for achieving efficient figure mining.

• 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

• TogoDoc server/client system: smart recommendation and efficient management of life science literature.

  Authors: Wataru Iwasaki, Yasunori Yamamoto, Toshihisa Takagi

  PloS one. 01/2010; 5(12):e15305.

  In this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word forIn this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word for integration). The server and the client program cooperate closely over the Internet to provide life scientists with an effective literature recommendation service and efficient literature management. The content-based and personalized literature recommendation helps researchers to isolate interesting papers from the "tsunami" of literature, in which, on average, more than one biomedical paper is added to MEDLINE every minute. Because researchers these days need to cover updates of much wider topics to generate hypotheses using massive datasets obtained from public databases or omics experiments, the importance of having an effective literature recommendation service is rising. The automatic recommendation is based on the content of personal literature libraries of electronic PDF papers. The client program automatically analyzes these files, which are sometimes deeply buried in storage disks of researchers' personal computers. Just saving PDF papers to the designated folders makes the client program automatically analyze and retrieve metadata, rename file names, synchronize the data to the server, and receive the recommendation lists of newly published papers, thus accomplishing effortless literature management. In addition, the tag suggestion and associative search functions are provided for easy classification of and access to past papers (researchers who read many papers sometimes only vaguely remember or completely forget what they read in the past). The TogoDoc system is available for both Windows and Mac OS X and is free. The TogoDoc Client software is available at http://tdc.cb.k.u-tokyo.ac.jp/, and the TogoDoc server is available at https://docman.dbcls.jp/pubmed_recom.

• 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.

• Figure Classification in Biomedical Literature towards Figure Mining.

  Authors: Natsu Ishii, Asako Koike, Yasunori Yamamoto, Toshihisa Takagi

  2008 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2008, 3-5 November 2008, Philadephia, Pennsylvania, USA; 01/2008

• Biomedical knowledge navigation by literature clustering.

  Authors: Yasunori Yamamoto, Toshihisa Takagi

  Journal of biomedical informatics. 05/2007; 40(2):114-30.

  There is an urgent need for a system that facilitates surveys by biomedical researchers and the subsequent formulation of hypotheses based on the knowledge stored in literature. One approach is toThere is an urgent need for a system that facilitates surveys by biomedical researchers and the subsequent formulation of hypotheses based on the knowledge stored in literature. One approach is to cluster papers discussing a topic of interest and reveal its sub-topics that allow researchers to acquire an overview of the topic. We developed such a system called McSyBi. It accepts a set of citation data retrieved with PubMed and hierarchically and non-hierarchically clusters them based on the titles and the abstracts using statistical and natural language processing methods. A novel point is that McSyBi allows its users to change the clustering by entering a MeSH term or UMLS Semantic Type, and therefore they can see a set of citation data from multiple aspects. We evaluated McSyBi quantitatively and qualitatively: clustering of 27 sets of citation data (40643 different papers) and scrutiny of several resultant clusters. While non-hierarchical clustering provides us with an overview of the target topic, hierarchical clustering allows us to see more details and relationships among citation data. McSyBi is freely available at http://textlens.hgc.jp/McSyBi/.

• OReFiL: an online resource finder for life sciences.

  Authors: Yasunori Yamamoto, Toshihisa Takagi

  BMC bioinformatics. 02/2007; 8:287.

  BACKGROUND: Many online resources for the life sciences have been developed and introduced in peer-reviewed papers recently, ranging from databases and web applications to data-analysis software.BACKGROUND: Many online resources for the life sciences have been developed and introduced in peer-reviewed papers recently, ranging from databases and web applications to data-analysis software. Some have been introduced in special journal issues or websites with a search function, but others remain scattered throughout the Internet and in the published literature. The searchable resources on these sites are collected and maintained manually and are therefore of higher quality than automatically updated sites, but also require more time and effort. DESCRIPTION: We developed an online resource search system called OReFiL to address these issues. We developed a crawler to gather all of the web pages whose URLs appear in MEDLINE abstracts and full-text papers on the BioMed Central open-access journals. The URLs were extracted using regular expressions and rules based on our heuristic knowledge. We then indexed the online resources to facilitate their retrieval and comparison by researchers. Because every online resource has at least one PubMed ID, we can easily acquire its summary with Medical Subject Headings (MeSH) terms and confirm its credibility through reference to the corresponding PubMed entry. In addition, because OReFiL automatically extracts URLs and updates the index, minimal time and effort is needed to maintain the system. CONCLUSION: We developed OReFiL, a search system for online life science resources, which is freely available. The system's distinctive features include the ability to return up-to-date query-relevant online resources introduced in peer-reviewed papers; the ability to search using free words, MeSH terms, or author names; easy verification of each hit following links to the corresponding PubMed entry or to papers citing the URL through the search systems of BioMed Central, Scirus, HighWire Press, or Google Scholar; and quick confirmation of the existence of an online resource web page.

• [Computational analyses of biomedical literature for understanding living organisms]

  Authors: Yasunori Yamamoto, Toshihisa Takagi

  Tanpakushitsu kakusan koso. Protein, nucleic acid, enzyme. 01/2006; 50(16 Suppl):2244-9.

• A Sentence Classification System for Multi Biomedical Literature Summarization.

  Authors: Yasunori Yamamoto, Toshihisa Takagi

  01/2005

• Evaluation System for Information Retrieval in Biomedical Field

  Authors: Yasunori Yamamoto, Toshihisa Takagi

  07/2004;

  Introduction Information Retrieval (IR) technology has grown rapidly accompanied by the development of the advanced computational power. More information is being stored as electro-magnetic forms,Introduction Information Retrieval (IR) technology has grown rapidly accompanied by the development of the advanced computational power. More information is being stored as electro-magnetic forms, and anyone can access to it with a PC beyond time and space [1]. Biomedical field is no exception as you know well. Thanks to IR (i.e., PubMed search), you can easily get literature information related to your research area from the vast amount of data which is otherwise intractable [2]. Thus, the aim of IR is to alleviate the cost for you to find your most wanted information. When developing an IR system, you need to evaluate it, but it is time consuming and needs people having thorough knowledge in the area the IR system focuses on. Usually, filed experts evaluate the system by using it in a practical situation. In this situation, you need to provide them with a comprehensive user interface to e#ciently and e#ectively collect the data [1]. To meet this demand, we developed an IR evaluati

• A Practical Partial Parser for Biomedical Literature Summarization.

  Authors: Yasunori Yamamoto, Toshihisa Takagi

  Natural Language Understanding and Cognitive Science, Proceedings of the 1st International Workshop on Natural Language Understanding and Cognitive Science, NLUCS 2004, In conjunction with ICEIS 2004, Porto, Portugal, April 2004; 01/2004

• A Method to Retrieve Papers from MEDLINE: PETER System.

  Authors: Hiroko Ao, Yasunori Yamamoto, Toshihisa Takagi

  01/2003

• Automatic Construction of Knowledge Base from Biological Papers.

  Authors: Yoshihiro Ohta, Yasunori Yamamoto, Tomoko Okazaki, Ikuo Uchiyama, Toshihisa Takagi

  Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology, Halkidiki, Greece, June 21-26, 1997; 01/1997