Abstract: Collecting, analyzing and extracting useful information from a very large amount of biomedical texts is a difficult task for researchers in biomedicine who need to keep up with scientific advances. Nowadays several domains in medical practice, drug development, and health care require support for such actives such as bioinformatics, medical informatics, clinical genomics, and many other sectors. Moreover, for this particular task, the data to be examined (i.e. textual data) are generally unstructured as in the case of Medline abstracts and the available resources (e.g. PubMed) and as many other textual resources such as medical records, patents etc. and they do not still provide adequate mechanisms for retrieving the required information as well as to help humans in “deeply analyse” very large amount of content. In this work we present a Text-Mining framework aiming to support biomedical researchers in the task of disease-genes relationships identification from scientific abstracts retrieved by querying Medline.