Multistage gene normalization and SVM-based ranking for protein interactor extraction in full-text articles

Hong Jie Dai, Po Ting Lai, Richard Tzong Han Tsai

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

The interactor normalization task (INT) is to identify genes that play the interactor role in protein-protein interactions (PPIs), to map these genes to unique IDs, and to rank them according to their normalized confidence. INT has two subtasks: gene normalization (GN) and interactor ranking. The main difficulties of INT GN are identifying genes across species and using full papers instead of abstracts. To tackle these problems, we developed a multistage GN algorithm and a ranking method, which exploit information in different parts of a paper. Our system achieved a promising AUC of 0.43471. Using the multistage GN algorithm, we have been able to improve system performance (AUC) by 1.719 percent compared to a one-stage GN algorithm. Our experimental results also show that with full text, versus abstract only, INT AUC performance was 22.6 percent higher.

Original languageEnglish
Article number5467043
Pages (from-to)412-420
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume7
Issue number3
DOIs
StatePublished - 2010

Keywords

  • Data mining
  • feature evaluation and selection
  • mining methods and algorithms
  • scientific databases
  • text mining

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