1 - 9 of 9 results

PESCADOR / Platform for Exploration of Significant Concepts AssociateD to co-Occurrences Relationships

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A web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. PESCADOR uses pre-compiled dictionaries of terms (from Entrez Gene and UniProt) for every organism with deposited genes (NCBI Taxonomy Database) and dictionaries of biological concepts (Medical Subject Headings, MeSH). Therefore, biologists need to simply load (copy/paste) their literature of interest (a list of PubMed identifiers, PMIDs) to launch the text-mining analysis.

Antimicrobial Combination Networks

Supports the mining of scientific literature and enables the reconstruction of recently documented antimicrobial combinations. Antimicrobial Combination Networks contains data on antimicrobial combinations that have been experimentally tested against Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans, which are prominent pathogenic organisms and are well-known for their wide and growing resistance to conventional antimicrobials. Researchers are able to explore the experimental results for a single organism or across organisms.

BELIEF / Biological Expression Language Information Extraction WorkFlow

Accelerates information extraction from the biomedical literature and curate causal and correlative relationships encoded into biological expression language. BELIEF uses a text mining pipeline to extract relation-ships from literature and a web curation that supports the visualization and curation of statements and context annotations automatically extracted by the pipeline. The curation interface was evaluated based on its performance and a user survey. Result showed that the BELIEF dashboard increased the curation efficiency when compared with manual curation.

MAT / MetastasisWay Annotation Tool

A network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MAT can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. A MAT network consists of different biological relationships between the concepts such as gene-gene regulation, gene-cancer regulation and the organs of the metastasis. This text mining service uses the principle-pattern-based approach to extract these relations. MET could prove very useful, especially in the construction of a database for metastasis networks.


Supports flexible network data association specification using rules, integrates data processing through relational databases and GML data files, and scalable data visualization through layered annotations. ProteoLens is a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools.