MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data.
BACKGROUND
In the absence of consolidated pipelines to archive biological data electronically, information dispersed in the literature must be captured by manual annotation. Unfortunately, manual annotation is time consuming and the coverage of published interaction data is therefore far from complete. The use of text-mining tools to identify relevant publications and to assist in the initial information extraction could help to improve the efficiency of the curation process and, as a consequence, the database coverage of data available in the literature. The 2006 BioCreative competition was aimed at evaluating text-mining procedures in comparison with manual annotation of protein-protein interactions.


RESULTS
To aid the BioCreative protein-protein interaction task, IntAct and MINT (Molecular INTeraction) provided both the training and the test datasets. Data from both databases are comparable because they were curated according to the same standards. During the manual curation process, the major cause of data loss in mining the articles for information was ambiguity in the mapping of the gene names to stable UniProtKB database identifiers. It was also observed that most of the information about interactions was contained only within the full-text of the publication; hence, text mining of protein-protein interaction data will require the analysis of the full-text of the articles and cannot be restricted to the abstract.


CONCLUSIONS
The development of text-mining tools to extract protein-protein interaction information may increase the literature coverage achieved by manual curation. To support the text-mining community, databases will highlight those sentences within the articles that describe the interactions. These will supply data-miners with a high quality dataset for algorithm development. Furthermore, the dictionary of terms created by the BioCreative competitors could enrich the synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular Interactions) controlled vocabulary, which is used by both databases to annotate their data content.
Chatr-aryamontri
Andrew
A
Department of Biology, University of Rome, Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy.
Kerrien
Samuel
S
Khadake
Jyoti
J
Orchard
Sandra
S
Ceol
Arnaud
A
Licata
Luana
L
Castagnoli
Luisa
L
Costa
Stefano
S
Derow
Cathy
C
Huntley
Rachael
R
Aranda
Bruno
B
Leroy
Catherine
C
Thorneycroft
Dave
D
Apweiler
Rolf
R
Cesareni
Gianni
G
Hermjakob
Henning
H
D019295:Computational Biology / Q000295:instrumentation / Q000379:methods* / Q000592:standards*; D016206:Databases, Bibliographic; D025941:Protein Interaction Mapping; D040901:Proteomics / Q000592:standards; D012958:Societies, Scientific; D018875:Vocabulary, Controlled