BRIEF RESEARCH REPORT article
Front. Bioinform.
Sec. Integrative Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1623488
This article is part of the Research TopicFrom codes to cells to care, transforming health care with AI – Proceedings of the 20th Annual Meeting of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS)View all 3 articles
Optimizing Clustering of CDR3 Sequences Using Natural Language Processing, Word2Vec, and KMeans
Provisionally accepted
- University of Dallas, Irving, United States
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T-cell receptor (TCR) sequencing has emerged as a powerful tool for understanding adaptive immune responses, yet challenges persist in deciphering the immense diversity of Complementarity-Determining Region 3 (CDR3) sequences. This study presents a novel natural language processing (NLP)-based pipeline to cluster CDR3 sequences from TCR β-chain repertoires using Word2Vec embeddings, principal component analysis (PCA), and KMeans clustering. Focusing on Acute Respiratory Distress Syndrome (ARDS), a life-threatening inflammatory lung condition, we trained Word2Vec models on healthy controls and applied unsupervised clustering across ARDS, non-ARDS, and control datasets. Dimensionality-reduced embeddings revealed clear distinctions in repertoire structure: control samples exhibited tight, low-diversity clusters; ARDS patients showed high dispersion and numerous diffuse clusters indicative of repertoire disruption; and non-ARDS samples displayed intermediate organization. These differences suggest that immune activation states are embedded in the structural topology of the CDR3 space. Our framework successfully captured these latent patterns, offering a scalable approach to biomarker discovery. This study not only reinforces the utility of NLP in immunological analysis but also paves the way for data-driven immune monitoring in critical care and personalized diagnostics.
Keywords: acute respiratory disease syndrome (ARDS), BioNLP, Bioinformatics & Computational Biology, Word2vec, Unsupervised learning
Received: 06 May 2025; Accepted: 16 Sep 2025.
Copyright: © 2025 Baranwal, Sanchez, Edet, Chastain and Toby-Ogundeji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Erick J Chastain, doctorilluminatus@gmail.com
Inimary Toby-Ogundeji, itoby@udallas.edu
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