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

Alaa Abi-Haidar and Luis M. Rocha

School of Informatics, Indiana University, 919 East Tenth Street, Bloomington IN 47408, USA
FLAD Computational Biology Collaboratorium, Instituto Gulbenkian de Ciencia, Portugal

Citation: A. Abi-Haidar and L.M. Rocha [2010]. "Biomedical Article Classification Using an Agent-Based Model of T-Cell Cross-Regulation". In: Artificial Immune Systems: 9th International Conference, (ICARIS 2010). E. Hart, C. McEwan, J. Timmis, and A. Hone (Eds.) Lecture Notes in Computer Science. Springer-Verlag, 6209.: 237-249. doi: 10.1007/978-3-642-14547-6_19.BibTex

The reprint is available from Springer; Our pre-print is also available in Adobe Acrobat (.pdf) format only.


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.

Keywords:Artificial Immune System, Bio-medical Document Classification, T-cell Cross-Regulation, Bio-inspired Computing, Artificial Intelligence.

For the full paper please download the preprint in pdf

For more information contact Luis Rocha at Check the Web Design Credits, for due credit.
Last Modified: June 22, 2010