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Evolutionary Intelligence

, Volume 4, Issue 2, pp 69–80 | Cite as

Collective classification of textual documents by guided self-organization in T-Cell cross-regulation dynamics

  • Alaa Abi-Haidar
  • Luis M. Rocha
Special Issue

Abstract

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 systems Biomedical document classification Data mining Machine learning Bio-inspired computing Complex adaptive systems Guided self-organization 

Notes

Acknowledgments

This work was partially supported by a grant from the FLAD Computational Biology Collaboratorium at the Instituto Gulbenkian de Ciencia in Portugal. We also thank the ICARIS2010 committee board for encouraging this work. We acknowledge the computational resources provided by Indiana University used to conduct the simulations we report.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.School of Informatics and ComputingIndiana UniversityBloomingtonUSA
  2. 2.FLAD Computational Biology CollaboratoriumInstituto Gulbenkian de CiênciaOeirasPortugal

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