Pavel B. Dobrokhotov

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MOTIVATION Searching relevant publications for manual database annotation is a tedious task. In this paper, we apply a combination of Natural Language Processing (NLP) and probabilistic classification to re-rank documents returned by PubMed according to their relevance to Swiss-Prot annotation, and to identify significant terms in the documents. RESULTS(More)
The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway,(More)
The goal of medical annotation of human proteins in Swiss-Prot is to add features specifically intended for researchers working on genetic diseases and polymorphisms. For this purpose, it is necessary to search through a vast number of publications containing relevant information. Promising results have been obtained by applying natural language processing(More)
Bio-medical knowledge bases are valuable resources for the research community. Original scientific publications are the main source used to annotate them. Medical annotation in Swiss-Prot is specifically targeted at finding and extracting data about human genetic diseases and polymorphisms. Curators have to scan through hundreds of publications to select(More)
RÉSUMÉ. Le travail que nous présentons ici a pour but la comparaison de méthodes de sélection d’attributs. Plus précisément, nous nous intéressons à deux grandes approches, celles fondées uniquement sur les données, approche classique qui permet de ne se reposer, pour la construction de modèles de catégorisation, que sur un ensemble restreint, mais(More)
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