Biomedical Article Classification Using an Agent-Based Model of T-Cell Cross-Regulation

Abstract. We propose a novel bio-inspired solution for biomedical article classification. Our method draws from an existing model of T-cell cross-regulation in the vertebrate immune system (IS), which is a complex adaptive system of millions of cells interacting to distinguish between harmless and harmful intruders. Analogously, automatic biomedical article classification assumes that the interaction and co-occurrence of thousands of words in text can be used to identify conceptually-related classes of articles--at a minimum, two classes with relevant and irrelevant articles for a given concept (e.g. articles with protein-protein interaction information). Our agent-based method for document classification expands the existing analytical model of Carneiro et al. [1], by allowing us to deal simultaneously with many distinct T-cell features (epitomes) and their collective dynamics using agent based modeling. We already extended this model to develop a bio-inspired spam-detection system [2...

• Type: Conference
• Year: 2010
• Where: ICARIS
• Authors: Alaa Abi-Haidar, Luis Mateus Rocha
• Keywords: Artificial Intelligence, Biomedical Article Classification, Document Classification, ICARIS 2010, T-cell Cross-regulation