title: Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.
abstract: BACKGROUND
  Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task.
  
  
  METHODS
  We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly.
  
  
  RESULTS
  By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure.
  
  
  CONCLUSIONS
  Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.
authors.affiliation: Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA. min.song@njit.edu
authors.forename: Min
authors.initials: M
authors.lastname: Song
authors.forename: Hwanjo
authors.initials: H
authors.lastname: Yu
authors.forename: Wook-Shin
authors.initials: WS
authors.lastname: Han
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D016239:MEDLINE; D009323:Natural Language Processing; D060066:Protein Interaction Maps; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012660:Semantics; D014481:United States
chemical_list: D011506:Proteins