The research team from the AIIA Lab, Institute of Information Science, Academia Sinica, Taiwan, led by Chun-Nan Hsu, and I-fang Chung's Lab at the Institute of Bioinformatics, National Yang-Ming University, achieved the second and third highest scores for the two methods that they submitted in the second BioCreative Challenge Evaluation, held in Madrid, Spain. There are 21 participants submitted their methods to this Challenge. The top score was achieved by a team from IBM T.J. Watson Research Center in the USA. However, since the organizer reported that the top 3 scores did not have statistically significantly differences, they can all be considered as the top scores in this Challenge. Moreover, after reweighting the sample to correct the sample selection bias, the score of the first method by Hsu and Chung becomes the top 1 among all participants.

        This Challenge is to evaluate the performance of state-of-the-art computer programs for the task of extracting gene and gene product mentions from a large corpus of literature in molecular biology. Such computer programs can assist molecular biologists to search literature related to certain genes. They also allow researchers to extract a large number of reports on certain molecular biology events (e.g., protein-protein interactions, reaction pathways, etc.) from literature without performing resource-demanding and time-consuming experiments.

        Therefore, a great deal of efforts has been devoted to this research around the world. Extracting gene mentions is particularly difficult because authors rarely use standardized gene names and gene names naturally co-occur with other types that have similar morphology, and even similar context. The Academia Sinica-Yang Ming team applied machine learning algorithms to train conditional random fields and support vector machines from a corpus of 15,000 sample sentences to achieve their top scores. They have been studying efficient training algorithms for conditional random fields and already achieved promising results. Those results will be published soon.

        This research is supported by the National Research Program for Genomic Medicine (NRPGM), National Science Council (NSC) under the grant for Advanced Bioinformatics Core (ABC) facility. ABC consists of four teams from National Yang-Ming University and Academia Sinica. ABC welcomes collaboration proposals from biology labs to extend the impact of their research achievements. Other participants include teams from the University of Pennsylvania, which was the defending top scorer, National Center for Biotechnology Information (NCBI), Cambridge University and other renowned research institutioins from Netherland, Spain, Germany, Korea, China etc.