A semi-supervised, weighted pattern-learning approach for extraction of gene regulation relationships from scientific literature

Yi Tsung Tang, Hung Yu Kao, Shaw Jenq Tsai, Hei Chia Wang

研究成果: Article同行評審

摘要

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.

原文English
頁(從 - 到)401-416
頁數16
期刊International Journal of Data Mining and Bioinformatics
9
發行號4
DOIs
出版狀態Published - 2014

All Science Journal Classification (ASJC) codes

  • 資訊系統
  • 一般生物化學,遺傳學和分子生物學
  • 圖書館與資訊科學

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