{"doi":"10.1101/2020.11.05.370312","title":"SwarmTCR: a computational approach to predict the specificity of T Cell Receptors","abstract":"Abstract Motivation Computationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. With more T cell receptor sequence data becoming available, the need for bioinformatics approaches to tackle this problem is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance. Results We compared the performance of SwarmTCR against a state-of-the-art method (TCRdist) and showed that SwarmTCR performed significantly better on epitopes EBV-BRLF1 300 , EBV-BRLF1 109 , NS4B 214–222 with single cell data and epitopes EBV-BRLF1 300 , EBV-BRLF1 109 , IAV-M1 58 with bulk sequencing data (α and β chains). In addition, we show that the weights returned by SwarmTCR are biologically interpretable. Availability SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub ( https://github.com/thecodingdoc/SwarmTCR ) Contact dghersi@unomaha.edu","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":127579,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9571,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":569725,"name":"Larisa Kamga","orcid":"0000-0002-5307-6776","position":1,"is_corresponding":false},{"id":569724,"name":"Anna Gil","orcid":"0000-0002-2043-6122","position":2,"is_corresponding":false},{"id":127634,"name":"Katherine Daniela Luzuriaga Chávez","orcid":"0000-0002-5561-1787","position":3,"is_corresponding":false},{"id":569726,"name":"Liisa K. Selin","orcid":"0000-0002-6993-4333","position":4,"is_corresponding":false},{"id":345709,"name":"Dario Ghersi","orcid":"0000-0002-0630-0843","position":5,"is_corresponding":false},{"id":577869,"name":"Ryan Ehrlich","orcid":null,"position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-18T23:15:34.966380Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}