This work makes available all the relevant content from patents to the scientific community, decreasing drastically the time required for this task, and provides graphical interfaces to ease the use of these tools.

Biomedical literature is composed of an ever increasing number of publications in natural language. Patents are a relevant fraction of those, being important sources of information due to all the curated data from the granting process. However, their unstructured data turns the search of information a challenging task. To surpass that, Biomedical text mining (BioTM) creates methodologies to search and structure that data. Several BioTM techniques can be applied to patents. From those, Information Retrieval is the process where relevant data is obtained from collections of documents. In this work, a patent pipeline was developed and integrated into @Note2, an open-source computational framework for BioTM. This integration allows to run further BioTM tools over the patent documents, including Information Extraction processes as Named Entity Recognition or Relation Extraction.

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

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The construction of repositories with curated information about gene essentiality for organisms of interest in Biotechnology is a very relevant task, mainly in the design of cell factories for the enhanced production of added-value products. However, it requires retrieval and extraction of relevant information from literature, leading to high costs regarding manual curation. Text mining tools implementing methods addressing tasks as information retrieval, named entity recognition and event extraction have been developed to automate and reduce the time required to obtain relevant information from literature in many biomedical fields. However, current tools are not designed or optimized for the purpose of identifying mentions to essential genes in scientific texts. In this work, we propose a pipeline to automatically extract mentions to genes and to classify them accordingly to their essentiality for a specific organism. This pipeline implements a machine learning approach that is trained using a manually curated set of documents related with gene essentiality in yeast. This corpus is provided as a resource for the community, as a benchmark for the development of new methods. Our pipeline was evaluated performing resampling and cross validation over this curated dataset, presenting an accuracy of over 80%, and an f1-score over 75%.