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

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, 3]. Here we develop our agent-base model further, and test it on a dataset of publicly available full-text biomedical articles provided by the BioCreative challenge [4]. We study several new parameter configurations leading to encouraging results comparable to state-of-the-art classifiers. These results help us understand both T-cell cross-regulation and its applicability to document classification in general. Therefore, we show that our bio-inspired algorithm is a promising novel method for biomedical article classification and for binary document classification in general.