Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross-Regulation Dynamics

Alaa Abi-Haidar and Luis M. Rocha

School of Informatics and Computing, 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 [2011]. "Collective Classification of Textual Documents by Guided Self-Organization in T-Cell Cross-Regulation Dynamics". Evolutionary Intelligence. 4(2):69-80. DOI: 10.1007/s12065-011-0052-5.

The pre-print is also available from the Due to mathematical notation and graphics, only the abstract is presented here.


We present and study an agent-based model of T-Cell cross-regulation in the adaptive immune system, which we apply to binary classification. Our method expands an existing analytical model of T-cell cross-regulation (Carneiro et al. in Immunol Rev 216(1):48-68, 2007) that was used to study the self-organizing dynamics of a single population of T-Cells in interaction with an idealized antigen presenting cell capable of presenting a single antigen. With agent-based modeling we are able to study the self-organizing dynamics of multiple populations of distinct T-cells which interact via antigen presenting cells that present hundreds of distinct antigens. Moreover, we show that such self-organizing dynamics can be guided to produce an effective binary classification of antigens, which is competitive with existing machine learning methods when applied to biomedical text classification. More specifically, here we test our model on a dataset of publicly available full-text biomedical articles provided by the BioCreative challenge (Krallinger in The biocreative ii. 5 challenge overview, p 19, 2009). We study the robustness of our model's parameter configurations, and show that it leads to encouraging results comparable to state-of-the-art classifiers. Our results help us understand both T-cell cross-regulation as a general principle of guided self-organization, as well as its applicability to document classification. 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, Collective Classification, Collective Behavior, Collective Computation, Bio-medical Document Classification, T-cell Cross-Regulation, Bio-inspired Computing, Artificial Intelligence.

For more information contact Luis Rocha at Check the Web Design Credits, for due credit.
Last Modified: August 29, 2011