[nature machine intelligence] ImmunoStruct enables multimodal deep learning for immunogenicity prediction
Annotation of mutated peptide sequences with published or novel potential neoantigen descriptors
The official implementation of "Improving Antibody Humanness Prediction using Patent Data".
Code for ImmunoGeNN: Accelerating Early Immunogenicity Assessment for Generative Design of Biologics
Implementation of the Analytic Hierarchy Process (AHP) to obtain the immunogenicity score of wheat lines based on their celiac disease (CD) epitopes matches on alpha- and gamma-gliadins amplicons by NGS and the score for oligopeptides based on an IFN-g ELISpot assays with fresh peripherical blood mononuclear cells (PBMCs).
HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies
Master thesis project, with aim to investigate tools that can potentially predict immunogenicity of therapeutic proteins. Further, the aim is to create a therapeutic profiler to predict immunogenicity of nanobodies, using the tools with best performance.
Code and data associated with Billings et al., "High dose inactivated influenza vaccine inconsistently improves heterologous antibody responses in an elderly human cohort", JID 2025.
An ML-based immunogenicity predictor for T-cell epitopes of Mycobacterium Tuberculosis. It is purely based on amino acid sequences of epitope. Used Physicochemical descriptors and Amino Acid composition to make this proteomic data ML ready.
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