title: A semi-supervised, weighted pattern-learning approach for extraction of gene regulation relationships from scientific literature.
abstract: Moreover, the large amount of textual knowledge in the existing biomedical literature is growing rapidly, and the creation of manual patterns from the available literature is becoming more difficult. There is an increasing demand to extract potential generic regulatory relationships from unlabelled data sets. In this paper, we describe a Semi-Supervised, Weighted Pattern Learning method (SSWPL) to extract such generic regulatory information from the literature. SSWPL can build new regulatory patterns according to predefined initial patterns from unlabelled data in the literature. These constructed regulatory patterns are then used to extract generic regulatory information from PubMed abstracts. The results presented herein demonstrate that our method can be utilised to effectively extract generic regulatory relationships from the literature by using learned, weighted patterns through semi-supervised pattern learning.
authors.forename: Yi-Tsung
authors.initials: YT
authors.lastname: Tang
authors.forename: Hung-Yu
authors.initials: HY
authors.lastname: Kao
authors.forename: Shaw-Jenq
authors.initials: SJ
authors.lastname: Tsai
authors.forename: Hei-Chia
authors.initials: HC
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D004247:DNA / Q000737:chemistry; D057225:Data Mining / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D025941:Protein Interaction Mapping; D039781:PubMed; D012984:Software
chemical_list: D004247:DNA