摘要: Biomedical literature represents the primary source of experimental data and biological knowledge. This project aims to develop a text mining system for pathogens of biodefense relevance, focusing on mining pathogen-host protein-p... 展开 Biomedical literature represents the primary source of experimental data and biological knowledge. This project aims to develop a text mining system for pathogens of biodefense relevance, focusing on mining pathogen-host protein-protein interactions (PH-PPI). We developed a Support Vector Machine (SVM)-based system to identify abstracts containing PH-PPI information using an annotated corpus of 1360 MEDLINE abstracts as the training set. It achieved good performance on document classification with a precision of over 80 among top 50 ranked abstracts. The SVM-based method is further augmented with other text mining tools (such as PIE) for mining and tagging PPI information. As part of an effort in enabling text mining tools for real-world applications, we are developing a basic framework, iProLINK, to connect text mining tools with ontology and systems biology for the biomedical research community. The PH-PPI text mining system developed in the first year will be coupled with the iProXpress proteomic data analysis system into a "Pathogen Mining System" for the analysis of pathogen proteomics data. 收起