The goal of medical annotation of human proteins in Swiss-Prot is to provide researchers working on genetic diseases and polymorphisms with all the useful information. For this purpose, curators must access and search through a vast number of publications, in order to extract the relevant information. Promising results have been obtained by applying natural language processing and machine learning techniques for resolution of this problem. Our solution relies on a categorisation step that re-orders documents such that relevant articles are easier to access. Our first results show promising results: relevant documents are returned within the top 40% of the list, and about 60% of articles are relevant in the top part of the list (while only 15% of documents are relevant overall).
Categorisation de Documents PubMed pour l’Annotation Médicale dans SWISS-PROT
| Cyril Goutte, Pavel Dobrokhotov, Eric Gaussier, Anne-Lise Veuthey |
| EGC Conférence, Atelier "Fouille de données et recherche d'informations dans des bases de données multimédia semi-structurées", Lyon, France, January 22, 2003. |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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