A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text.

Abstract Summary: VTag is an application for identifying the type, genomic location and genomic state-change of acquired genomic aberrations described in text. The application uses a machine learning technique called conditional random fields. VTag was tested with 345 training and 200 evaluation documents pertaining to cancer genetics. Our experiments resulted in 0.8541 precision, 0.7870 recall and 0.8192 F-measure on the evaluation set. Availability: The software is available at http://www.cis.upenn.edu/group/datamining/software_dist/biosfier/.

Abstract Summary: We have adapted the originally described electronic PCR (e-PCR) algorithm to perform string searches more accurately and much more rapidly than previously possible. Our implementation [multithreaded e-PCR (me-PCR)] runs sufficiently fast to allow even desktop machines to query quickly large genomes with very large genomic element sets. In addition, me-PCR is multithreaded, interprets all IUPAC nucleotide symbols, allows searches with elements specified by long sequences (such as SNPs), accepts ranges in the expected PCR size input field, requires substantially less memory for analysis of large sequences and corrects a number of minor flaws causing misreporting of hits in exceptional cases. Thus, me-PCR provides increased annotation capabilities for complex genomes to non-expert laboratories. Availability: me-PCR has been compiled for Linux, Solaris, AIX, Windows and Macintosh platforms. Both source and executable code are freely available to the research community at http://genome.chop.edu/mePCR/