Biology subjects

Rei, M.

Publications and source records attributed to Rei, M..

6 recordsLinked to original sources

bioRxiv2026

Relation Extraction for Diet, Non-Communicable Disease and Biomarker Associations (RECoDe): A CoDiet study

Diet plays a critical role in human health, with growing evidence linking dietary habits to disease outcomes. However, extracting structured dietary knowledge from biomedical literature remains challenging due to the lack of dedicated relation extraction datasets. To address this gap, we introduce RECoDe, a novel relation extraction (RE) dataset designed specifically for diet, disease, and related biomedical entities. RECoDe captures a diverse set of relation types, including a broad spectrum of positive association patterns and explicit negative examples, with over 5,000 human-annotated instances validated by up to five independent annotators. Furthermore, we benchmark various natural language processing (NLP) RE models, including BERT-based architectures and enhanced prompting techniques with locally deployed large language models (LLMs) to improve classification performance on underrepresented relation types. The best performing model gpt-oss-20B, a local LLM, achieved an F1-score of 64% for multi-class classification and 92% for binary classification using a hierarchical prompting strategy with a separate reflection step built in. To demonstrate the practical utility of RECoDe, we introduce the Contextual Co-occurrence Summarisation (Co-CoS) framework, which aggregates sentence-level relation extractions into document-level summaries and further integrates evidence across multiple documents. Co-CoS produces effect estimates consistent with established dietary knowledge, demonstrating its validity as a general framework for systematic evidence synthesis. AvailabilityThe code, models, and data will be made freely available upon acceptance.

bioinformatics↗

bioRxiv2025

High-precision Biomedical Text Corpora for Multi-Entity Recognition: A CoDiet study

We present here five biomedical, multi-entity corpora that can be used as benchmarks for named-entity recognition (NER), targeted to literature on metabolic syndrome. The CoDiet-Gold corpus contains annotations for 500 full-text publications and 348,406 annotations. It is divided into CoDiet-Gold-public (450 documents) and CoDiet-Gold-private (50 documents). Each document was independently annotated by two human experts, with disagreements fully adjudicated by a third expert. The CoDiet-Electrum corpus (2,998,273 annotations) contains 4,423 publications that were annotated using case-insensitive matching of the surface forms with punctuation ignored, found in CoDiet-Gold-public. Finally, for the same 4,423 documents, two fully machine annotated corpora CoDiet-Bronze (2,938,738 annotations) and CoDiet-Silver (2,298,988 annotations), were created by utilising existing NER algorithms to annotate these. These corpora contain categories (organisms, disease, genes, proteins, metabolites) that add depth to existing corpora, as well as new categories that do not appear in other corpora (food, dietary methods, sample types, computational methods, study methodology, population characteristics, data types, and microbiome).

bioinformatics↗

bioRxiv2024

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

Understanding genetic variants effects on the epigenome is crucial for interpreting genome-wide association studies (GWAS) results, yet profiling these effects across the non-coding genome remains challenging due to the scalability limits of experimental methods. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we propose the development of a deep learning model, Enformer Celltyping, which can both incorporate distal effects of DNA interactions, up to 100,000 base-pairs away, and predict epigenetic signals in a cell type-agnostic fashion. We demonstrate that Enformer Celltypings predictions of the epigenome out-perform the current best-in-class approach, have strong performance in a range of different cell types and biological regions and generalise to cell types assayed independently of the original training set. Finally, we propose an approach to test epigenetic models performance on genetic variant effect predictions using regulatory quantitative trait loci mapping studies, highlighting Enformer Celltypings and other genomic deep learning models limitations for this task. Our work introduces a new, accurate model for cell type-agnostic histone mark predictions and highlights the benefit of transfer learning for more efficient model development for deep learning in genomics. We make our customisable and efficient transfer learning approach for Enformer available to the community.

genomics↗

bioRxiv2020

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells, is of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology, has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as SARS-CoV-2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in training data of the models seem to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen vs. cancer peptides. Overall, we demonstrate that accurate and reliable prediction of immunogenic CD8+ T cell targets remains unsolved, thus we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

immunology↗

bioRxiv2020

HLA-E restricted, HIV-1 suppressing, Gag specific CD8+ T cells offer universal vaccine opportunities

Human leukocyte antigen-E (HLA-E) normally presents a HLA class Ia signal peptide to the NKG2A/C-CD94 regulatory receptors on natural killer (NK) cells and T cell subsets. Rhesus macaques immunized with a cytomegalovirus vectored simian immunodeficiency virus (SIV) vaccine, generated Mamu-E (HLA-E homolog) restricted T cell responses that mediated post-challenge SIV replication arrest in >50% of animals. However, human immunodeficiency virus type 1 (HIV-1) specific HLA-E restricted T cells have not been observed in HIV-1-infected individuals. Here we primed HLA-E restricted HIV-1 specific CD8+ T cells in vitro. These T cell clones, and allogeneic CD8+ T cells transduced with their T cell receptors, suppressed HIV-1 replication in CD4+ T cells in vitro. Vaccine induction of efficacious HLA-E restricted HIV-1 specific T cells should therefore be possible. One Sentence SummaryCD8+ T cells that recognize a Gag peptide presented by HLA-E suppress HIV-1 replication in vitro.

immunology↗

bioRxiv2019

Semaphorin 3A induces cytoskeletal paralysis in tumor-specific CD8+ T cells

Semaphorin-3A (Sema3A) regulates tumor angiogenesis, but its role in modulating anti-tumor immunity is unclear. We demonstrate that Sema3A secreted within the tumor microenvironment (TME) suppresses tumor-specific CD8+ T cell function via Neuropilin-1 (NRP1), a receptor that is upregulated upon activation with T cells cognate antigen. Sema3A inhibits T cell migration, assembly of the immunological synapse, and tumor killing. It achieves these functional effects through hyper-activating the acto-myosin system in T cells leading to cellular paralysis. Finally, using a clear cell renal cell carcinoma patient cohort, we demonstrate that human tumor-specific CD8+ T cells express NRP1 and are trapped in Sema3A rich regions of tumors. Our study establishes Sema3A as a potent inhibitor of anti-tumor immunity.

immunology↗