English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We present and study an agent-based model of T-Cell cross-regulation in theadaptive immune system, which we apply to binary classification. Our methodexpands an existing analytical model of T-cell cross-regulation (Carneiro etal. in Immunol Rev 216(1):48-68, 2007) that was used to study theself-organizing dynamics of a single population of T-Cells in interaction withan idealized antigen presenting cell capable of presenting a single antigen.With agent-based modeling we are able to study the self-organizing dynamics ofmultiple populations of distinct T-cells which interact via antigen presentingcells that present hundreds of distinct antigens. Moreover, we show that suchself-organizing dynamics can be guided to produce an effective binaryclassification of antigens, which is competitive with existing machine learningmethods when applied to biomedical text classification. More specifically, herewe test our model on a dataset of publicly available full-text biomedicalarticles provided by the BioCreative challenge (Krallinger in The biocreativeii. 5 challenge overview, p 19, 2009). We study the robustness of our model'sparameter configurations, and show that it leads to encouraging resultscomparable to state-of-the-art classifiers. Our results help us understand bothT-cell cross-regulation as a general principle of guided self-organization, aswell as its applicability to document classification. Therefore, we show thatour bio-inspired algorithm is a promising novel method for biomedical articleclassification and for binary document classification in general.

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