title: An IR-aided machine learning framework for the BioCreative II.5 Challenge.
abstract: The team at the University of Wisconsin-Milwaukee developed an information retrieval and machine learning framework. Our framework requires only the standardized training data and depends upon minimal external knowledge resources and minimal parsing. Within the framework, we built our text mining systems and participated for the first time in all three BioCreative II.5 Challenge tasks. The results show that our systems performed among the top five teams for raw F1 scores in all three tasks and came in third place for the homonym ortholog F1 scores for the INT task. The results demonstrated that our IR-based framework is efficient, robust, and potentially scalable.
authors.affiliation: College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA. chiefadminofficer@gmail.com
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Cao
authors.forename: Zuofeng
authors.initials: Z
authors.lastname: Li
authors.forename: Feifan
authors.initials: F
authors.lastname: Liu
authors.forename: Shashank
authors.initials: S
authors.lastname: Agarwal
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhang
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D016247:Information Storage and Retrieval; D025941:Protein Interaction Mapping / Q000379:methods; D014922:Wisconsin