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Sumathipala, Sagara,Yamada, Koichi,Unehara, Muneyuki,Suzuki, Izumi Korean Institute of Intelligent Systems 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.2
Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.
Sagara Sumathipala,Koichi Yamada,Muneyuki Unehara,Izumi Suzuki 한국지능시스템학회 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.2
Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.
Self-evolving Disease Ontology for Medical Domain Based on Web
Ishara Sandun,Sagara Sumathipala,Gamage Upeksha Ganegoda 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.4
In last decade information technology has gained a rapid development, and today it plays a crucial role in everyone’s life. It makes the life more comfortable for professional to do their work. Every performance and the innovating task will become more comfortable if there is a proper and accurate knowledge base containing up to date information. It will be an added advantage if the so-called knowledge base could shrink and expand dynamically. Especially in the medical domain, there is a higher demand and necessity for such kind of knowledge base which evolves dynamically with time and data because medical field is rapidly evolving and new biomedical entities such as diseases, symptoms, proteins, and so forth are frequently introducing. This study proposes a mechanism to generate dynamically evolving ontology for the biomedical domain which evolves with new relations explores from web data and patient history records. Proposed approach retrieves information from the ontology and generates probabilistic values for each relationship in the disease ontology. This approach used to create a dynamically evolving ontology for the medical domain to manage the relationship between diseases and symptoms more effectively. Furthermore, it retrieves data from the ontology to answer user queries related to the diseases and symptoms.
International Network of Twin Registries (INTR): Building a Platform for International Collaboration
Buchwald, Dedra,Kaprio, Jaakko,Hopper, John L.,Sung, Joohon,Goldberg, Jack,Fortier, Isabel,Busjhan, Andreas,Sumathipala, Athula,Cozen, Wendy,Mack, Thomas,Craig, Jeffrey M.,Harris, Jennifer R. Cambridge University Press 2014 TWIN RESEARCH AND HUMAN GENETICS - Vol.17 No.6
<P>The International Network of Twin Registries (INTR) aims to foster scientific collaboration and promote twin research on a global scale by working to expand the resources of twin registries around the world and make them available to researchers who adhere to established guidelines for international collaboration. Our vision is to create an unprecedented scientific network of twin registries that will advance knowledge in ways that are impossible for individual registries, and includes the harmonization of data. INTR will also promote a broad range of activities, including the development of a website, formulation of data harmonization protocols, creation of a library of software tools for twin studies, design of a search engine to identify research partners, establishment of searchable inventories of data and biospecimens, development of templates for informed consent and data sharing, organization of symposia at International Society of Twin Studies conferences, support for scholar exchanges, and writing grant proposals.</P>