Applying text mining and machine learning techniques to gene clusters analysis

Genomic data clustering has been receiving a growing attention during last years. However, finding the biological meaning of the clusters is still a manual work, which becomes very difficult as the amount of data grows. In this paper, the authors present a few experiments applying Text Mining and Machine Learning techniques to help associating meaning to genes clusters. These experiments were applied to papers abstracts and interaction database data related to Saccaromyces cerevisiae genes both for identifying texts content and for explaining the biological meaning of the genes clusters found. The results were compared to information published by experts in molecular biology and a number of relevant equivalences were found.