A Generic Statistical Method for
 Information Extraction in Genomics  

Violaine Pillet
INRIA Rhône-Alpes
655 avenue de l'Europe
Montbonnot, France, 38330
Violaine.Pillet@inrialpes.fr	Denys Proux
Xerox Research Centre Europe 
6 Chemin de Maupertuis
Meylan, France, 38000
Denys.Proux@xrce.xerox.com	François Rechenmann
INRIA Rhône-Alpes
655 avenue de l'Europe
Montbonnot, France, 38330
Francois.Rechenmann@inria.fr	

Despite a tendency to publish results both in well specified databases and journals or proceedings, most of the available information is still described in the literature. Furthermore, databases generally reflect a selected view on a particular domain. The data structure is oriented by the semantics pertinent to those who create and use in a first hand these databases. To access cross-domain information or information linked to slightly different points of view, researchers need to mine several databases to extract selected information and resolve dependency links to merge all these data into structure that fit their interests. But, it does not necessarily result that all available information can be found in databases. The greatest part of information is to be found in informally structured descriptions in texts. 	This fact has led us to develop a generic method to extract information from texts. To design and validate the method we have considered a specific case, genetic interactions for Drosophila Melanogaster. This project was supervised by Luc Quoniam (CRRM) and Bernard Jacq (LGPD-CNRS).
The method has been set-up using the Flybase database. Relevant information for the experimentation has been identified within the base. Sentences have been selected and normalized and gene names have been identified. These sentences have then been filtered to keep only those with two gene names, assuming that they might deal with interactions. This corpus of 1200 sentences has been validated by experts in order to build two clusters : the first one contains sentences describing molecular and genetic interaction descriptions and the second one includes sentences containing no interaction descriptions. 
A two steps strategy has been proposed. The first step is a learning phase which makes use of a statistical analysis performed on the corpus to calculate weights for sentence words and to determine a threshold. The second step is the application of the weights and threshold to discriminate sentences dealing with interactions on a new corpus. This strategy applied to the Flybase learning corpus has produced the following results: detection of 87.3% of sentences describing interactions with only 19.3% of false-positives. The recall and precision rates are respectively 87.4% and 85.7%. The early results on the learning corpus were encouraging, but to validate the method an evaluation has been made on a new blind corpus composed of Medline abstracts related to genomics for Drosophila Melanogaster. This experiment has been done on a set of 313 sentences including 104 sentences dealing with interactions. for The recall and precision ratios are respectively 74% and 51.7%. Both corpora are available for cross-evaluation and benchmarking. This work shows that it is possible to discriminate sentences that deal with interactions thanks to their content.
		However a close analysis of these results has highlighted the importance of an efficient entity detection mechanism. To be able to extract semantic relations between entities, the obvious but crucial thing to do is to identify the entities in texts. It can easily be done if dictionaries or strict morphologic rules are available. But generally it is not the case, and even if those resources are accessible, unregistered synonyms are common and morphologic rules are far from being respected. These considerations have led us to initiate a project aiming at designing and implementing a new and efficient automatic entity detection mechanism relying on lexicons, morphologic rules and also powerful natural language processing capabilities. The goal of such a tool is to improve the global information extraction process and also to detect new entities in texts.