报告题目：Towards efficient data integration and knowledge management for rice research
1 ICT Lab & LMI Rice, USTH, Hanoi, Vietnam.
2 UMR DIADE, IRD, Montpellier, France.
3 South Green Bioinformatics Platform, Montpellier France.
时 间：2018年6月5日，1:30 - 3:00 pm
CV: Prof Pierre Larmande got his PhD degree in computer science – University of Montpellier in France at 2007, Master degree in computer science – University of Montpellier in France at 2000 and Master degree in biochemstry at 1996. Since 2016 he is the Visiting Scientist of USTH University of Hanoi (Vietnam) and Agricultural Genetics Institute (AGI) at LMI RICE & ICTLab. He is also the Chair of the Rice Data Interoperability Working Group (http://rd-alliance.org) and the Chair of the 2016 PhenoHarmonIS workshop (http://tinyurl.com/COcommunity).
Unit DIADE (Plant Diversity Adaptation and Development) - IRD
911 avenue Agropolis, BP 64501, 34394 Montpellier cedex 5, France,
Email: email@example.com Phone : +84 1 666 018 725
Abstract: Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the rice research domain. This data explosion in-conjunction with its heterogeneity presents a major challenge in adopting an integrative approach towards research. There is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. To this end, the Semantic Web and machine learning approaches offer a stack of powerful technologies for the integration of information from diverse sources and make knowledge explicit thanks to ontologies. We have developed AgroLD, an RDF knowledge-based system that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on rice species and in this way facilitating the formulation of new scientific hypotheses. RDF describes a resource and its relationships/properties in the form of simple triples, i.e., Subject-Predicate-Object offering a very convenient framework for integrating data across multiple platforms assuming the platforms share some common vocabularies to describe their objects. These triples can be combined to construct large networks of information (also known as RDF graphs). The objective of this effort is to provide the community with a platform for domain specific knowledge, capable of answering complex biological questions. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. Currently, AgroLD contains hundreds millions of triples created by annotating more than 50 datasets coming from 10 data sources such as Gramene.org  and TropGeneDB  with 10 ontologies such as Gene Ontology  and Plant Trait Ontology . Our objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.
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