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Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.
Brief Bioinform
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129 Shaanxi, China.
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I molecules and the recognition of the peptide-MHC-I (pMHC-I) complex by T cell receptors (TCRs). Accurate prediction of pMHC-I binding and TCR recognition remains a significant computational challenge in immunology due to intricate binding motifs and the long-tail distribution of known binding pairs in public databases.
View Article and Find Full Text PDFA comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.
Brief Bioinform
November 2024
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.
The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.
View Article and Find Full Text PDFMapping Key Populations to Develop Improved HIV and AIDS Interventions: Multiphase Cross-Sectional Observational Mapping Study Using a District and City Approach.
JMIR Public Health Surveill
January 2025
Monitoring, Evaluation, and Learning Platform USAID, Jakarta, Indonesia.
Background: Indonesia's vast archipelago and substantial population size present unique challenges in addressing its multifaceted HIV epidemic, with 90% of its 514 districts and cities reporting cases. Identifying key populations (KPs) is essential for effectively targeting interventions and allocating resources to address the changing dynamics of the epidemic.
Objective: We examine the 2022 mapping of Indonesia's KPs to develop improved HIV and AIDS interventions.
Development and Validation of Rabies Health Education Module (RaHEM) for Dog Owners in Kelantan, Malaysia: An ADDIE Model.
J Epidemiol Glob Health
January 2025
Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia.
Background: Rabies is a preventable yet deadly public health threat. Despite the availability of effective vaccines for both humans and animals, the persistence of rabies-related fatalities underscores the need for enhanced public education strategies. This study aimed to develop and validate a Rabies Health Education Module delivered via a Massive Open Online Course, targeting adult dog owners in Kelantan, Malaysia.
View Article and Find Full Text PDFAn explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study.
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu City, Sichuan Province, 610041, China.
Background: Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade.
Methods: This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts.
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