Biology subjects

Tan, W. K.

Publications and source records attributed to Tan, W. K..

3 recordsLinked to original sources

bioRxiv2026

AI4Loop: an Artificial Intelligence Framework Reveals Increased 3D Chromatin Interactions and Therapeutic Vulnerabilities across 12,000 Cancer Samples

Three-dimensional chromatin interactions shape gene regulation, but their large-scale analysis remains limited by the cost and complexity of experimental assays. Here we present AI4Loop, a deep learning framework that infers genome-wide gene-centered chromatin interaction networks directly from RNA-seq data. Across multiple cell types, AI4Loop recovered interaction patterns consistent with clinical samples and orthogonal chromatin conformation datasets. Applied to 12,347 transcriptomes from 32 cancer types, AI4Loop revealed pervasive increases in gene-centered chromatin interactions in tumors, particularly at oncogene-associated loci. These inferred interaction networks outperformed gene expression alone in cancer classification. Integration with more than 50,000 drug-treated transcriptomes identified compounds predicted to reverse cancer-associated interaction gains. Hi-C experiments confirmed that the oxazolidinone antibiotics eperezolid and radezolid reduce breast cancer-gain chromatin interactions. Together, these results identify increased gene-centered chromatin interactions as a pan-cancer feature and provide a scalable strategy for linking 3D genome dysregulation to therapeutic vulnerabilities.

bioinformatics↗

bioRxiv2025

A Domain-Specific Language Model Approach for Identifying Immunogenic Epitopes from Publicly Available Data: EpitopeMiner

Personalized cancer vaccines represent a transformative approach in immunotherapy, leveraging tumor-specific antigens, such as neoantigens, to stimulate durable and targeted immune responses. However, identifying immunogenic neoantigen epitopes through conventional approaches - relying on in silico predictions followed by experimental validation - remains a significant challenge due to limitations of computational tools and scattering of experiment data. To address these challenges, we introduce EpitopeMiner, a domain-specific large language model (LLM) enhanced with a Retrieval-Augmented Generation (RAG) framework, specifically designed to identify immunogenic epitopes predicted by existing computational tools. EpitopeMiner utilizes a custom database of MHC-I-associated epitope-related literature to provide domain-specific knowledge, enhancing the precision and relevance of LLM-generated responses. EpitopeMiner offers three key features: First, the ability to identify epitopes with similar sequences and potentially similar immunogenic effects, which is especially valuable for neoantigens that are patient-specific and rarely found in public datasets. Second, it supports multiple epitope searches with structured outputs, enhancing scalability. Third, it provides original text chunks and paper identifiers, significantly simplifying validation and further exploration of the retrieved knowledge. Applying EpitopeMiner to well-characterized MHC class I epitopes demonstrates its ability to consistently retrieve relevant papers, efficiently providing targeted insights on T-cell response and immunogenicity, outperforming a commercial AI-powered literature tool. Notably, when applied to lymphoma patient-derived neoantigens, EpitopeMiner successfully retrieves immune response information for a few similar epitopes, despite operating with a relatively smaller database compared to the benchmark tool. All in all, EpitopeMiner bridges the gap between computational prediction and experimental validation, providing a scalable solution for extracting knowledge from public data, fostering cross-study synergies, and accelerating the development of personalized cancer vaccines.

cancer biology↗

bioRxiv2019

Efficient horizontal transmission without viral super-spreaders may cause the high prevalence of STLV-1 infection in Japanese macaques

AbstractsSimian T-cell leukemia virus type-1 (STLV-1) is disseminated among various non-human primate species and is closely related to human T-cell leukemia virus type-1 (HTLV-1), the causative agent of adult T-cell leukemia and HTLV-1-associated myelopathy/tropical spastic paraparesis. Notably, the prevalence of STLV-1 infection in Japanese macaques (JMs) is estimated to be much greater than that in other non-human primates; however, the mechanism and mode of STLV-1 transmission remain unknown. We hypothesized that a substantial proportion of infected macaques may play a critical role as viral super-spreaders for efficient inter-individual transmission leading to the high prevalence of infection. To address this, we examined a cohort of 280 JMs reared in a free-range facility for levels of anti-STLV-1 antibody titers (ABTs) and STLV-1 proviral loads (PVLs). We found that the prevalence of STLV-1 in the cohort reached up to 65% (180/280), however, the ABTs and PVLs were normally distributed with mean values of 4076 and 0.62%, respectively, which were comparable to those of HTLV-1-infected humans. Contrary to our expectations, we did not observe the macaques with abnormally high PVLs and poor ABTs, and therefore, the possibility of viral super-spreaders was unlikely. Results from further analyses regarding age-dependent changes in STLV-1 prevalence and a longitudinal follow-up of STLV-1 seroconversion strongly suggest that frequent horizontal transmission is a major route of STLV-1 infection, probably due to the unique social ecology of JMs associated with environmental adaptation.\n\nImportanceWe investigated the cause of the high prevalence of STLV-1 infection in the studied JMs cohort. Contrary to our expectations, the potential viral super-spreaders as shown by abnormally high PVLs and poor ABTs were not observed among the JMs. Rather, the ABTs and PVLs among the infected JMs were comparable to those of HTLV-1-infected humans although the prevalence of HTLV-1 in humans is much less than the macaques. Further analyses demonstrate that the prevalence drastically increased over one year of age and most of these animals over 6 years of age were infected with STLV-1, and that in the longitudinal follow-up study frequent seroconversion occurred in not only infants but also in juvenile and adult seronegative monkeys (around 20% per year). This is the first report showing that frequent horizontal transmission without viral super-spreaders may cause high prevalence of STLV-1 infection in JMs.

microbiology↗