Search Results for "text processing"

Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information.

NOTE: Project moved to https://github.com/MolBIFFM/PTGLtools. The Visualization of Protein-Ligand Graphs (VPLG) software package computes and visualizes protein graphs. It works on the super-secondary structure level and uses the atom coordinates from PDB files and the SSE assignments of the DSSP algorithm. VPLG is command line software. If you do not like typing commands, try our PTGL web server: http://ptgl.uni-frankfurt.de/

The BioNLP UIMA Component Repository provides UIMA wrappers for novel and well-known 3rd-party NLP tools used in biomedical text prosessing, such as tokenizers, parsers, named entity taggers, and tools for evaluation.

BioDare (Biological Data Repository) was developed under the multi-site ROBuST project (http://hallidaylab.bio.ed.ac.uk/ROBuST.html) to support data exchange inside the project. It is a web application which allows data-sharing (including public dissemination), data-processing and analysis, with the main focus on time-series data produced in circadian experiments. The main features of BioDare are: - an online repository for experimental data accompanied by extensive metadata - generation of secondary data (normalized, detrended, averaged …) - graphical output of data, secondary data and rhythm analysis - simple text-based search throughout metadata - biology- and conditions-aware search for data - data aggregation and export - group-based privacy settings for collaborative research

MutationFinder is a biomedical natural language processing (NLP) system for extracting mentions of point mutations from free text. MutationFinder achieves high performance (99% precision, 81% recall on blind test data) as an information extraction system