PaNTex: A novel methodology to assemble Pathway Networks using Text Mining (2 pag.)

JULIETA S. DUSSAUT; CRAVERO, FIORELLA; PONZONI, IGNACIO; MAGUITMAN, ANA G.; CECCHINI, ROCÍO L.

San Carlos de Bariloche

Congreso; V. Congreso Argentino de Bioinformática y Biología Computacional (5CAB2C); 2014

AB2C2 y el Instituto de Energía y Desarrollo Sustentable (IEDS), Centro Atómico Bariloche (CNEA).

Systems Biology is a discipline that integrates biological knowledge coming from different sources to study a range of complex biological regulatory system. In this context, the pathways, firstly created as a graphical representation of well-established knowledge about biological processes, are becoming increasingly important for life science research [1]. However the determination of interaction patterns in pathway networks are typically manual procedures which require significant contributions from domain experts within the research community. During the past years we have witnessed the emergence of novel data-driven methods aimed at assisting Systems Biology research. In particular, the analysis of information on molecular events contained in very large repositories has led to new approaches to extract biological interactions from scientific literature [2]. Literature mining methods can help analyze, integrate, and understand not only large collections of data per se, but also the linkages amongst them which allow us to make inferences [3, 4]. The fast publication of new papers make staying up-to-date a serious challenge (i.e. PubMed database contains information for over 23 million articles and continues to grow at a high rate weekly). Therefore, text mining methods, which aid in the construction and maintenance of pathway knowledge, have become relevant tools for biologists to manage this increasing quantity of biological literature. Another crucial issue in text mining applied to Bioinformatics is to achieve a robust testing of the methods due to the lack of large, objectively validated test sets or ?gold standards? [5]. These problems have as main consequence that many inferred pathways do not represent coherent explanations of the reported facts [3], and to transform the results of automatically constructed networks into pathways seems to require important additional human efforts. For that reason, the integration of literature mining algorithms with robust validation strategies for pathway knowledge extraction is an interesting open research field.