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

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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.

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Notes

  1. We use the terminology of self/nonself discrimination, though perhaps a more accurate description is classification of harmless vs. harmful substances; harmless can also include antigens from bacteria that are necessary for vertebrate bodies, and harmful can also include body’s own tumor cells.

  2. A good, though already a bit dated, overview of the vertebrate immune system for the artificial life community is Hofmeyer’s [12].

  3. The simplification of proliferation to mere duplication adopted in the canonical CRM model is maintained in our agent-based model to minimize the number of parameters (excluding proliferation rates) and the parameter search space

  4. Every E f or R f has equal probability of binding to the APC that presents feature f

  5. The list of common (stop) words includes 33 of the most common English words from which we manually excluded the word “with”, as we know it to be of importance to PPI

  6. For feature extraction we used both the training data of Biocreative 2.5 and Biocreative 2 as described in [11]; all classifiers used the exact same feature set.

  7. TF.IDF is a common text weighting measure to evaluate the importance of a feature/word in a document in a corpus. TF stands for term frequency in a document and IDF for inverse document frequency in the corpus.

  8. Notice that this parameter search on the provided labeled training data uses only the information available to the teams participating in Biocreative 2.5 challenge, and none of the test data whose labels were revealed post-challenge.

  9. \(\hbox{F-score} ={\frac{\hbox{2.Precision.Recall}}{\hbox{Precision} + \hbox{Recall}}}\) where \(\hbox{Precision} = {\frac{\hbox{TP}}{\hbox{TP} + \hbox{FP}}}\) and \(\hbox{Recall} ={\frac{\hbox{TP}}{\hbox{TP} + \hbox{FN}}}\). True Positives (TP) and False Positives (FP) are the classifier’s correct and incorrect predictions for relevant documents, while True Negatives (TN) and False Negatives (FN) are the correct and incorrect predictions for irrelevant documents.

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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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Authors and Affiliations

  1. School of Informatics and Computing, Indiana University, Bloomington, IN, 47401, USA

    Alaa Abi-Haidar & Luis M. Rocha

  2. FLAD Computational Biology Collaboratorium, Instituto Gulbenkian de Ciência, Oeiras, Portugal

    Alaa Abi-Haidar & Luis M. Rocha

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  1. Alaa Abi-Haidar
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  2. Luis M. Rocha
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Correspondence to Alaa Abi-Haidar.

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Abi-Haidar, A., Rocha, L.M. Collective classification of textual documents by guided self-organization in T-Cell cross-regulation dynamics. Evol. Intel. 4, 69–80 (2011). https://doi.org/10.1007/s12065-011-0052-5

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