Build Neoantigen Prediction Tool for Brain Therapeutics
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About this role
Python, Data Processing, Machine Learning (ML), Statistical Analysis, Data Science, Neural Networks, Documentation, Data Visualization, Deep Learning, Bioinformatics · JOB: Computational Biologist / ML Engineer - Brain-Specific Neoantigen Prediction Tool
PROJECT OVERVIEW: I am building a brain-specific neoantigen prediction tool for mRNA/LNP/PNA therapeutics. The goal is to predict which mutated peptides (neoantigens) are most likely to be presented by HLA in brain metastasis and trigger an immune response. This is a 7-stage pipeline with a defined step list.
WHAT YOU WILL BUILD:
Stage 1 - Data Sourcing - Search and download brain metastasis MS immunopeptidomics data from CPTAC, SysteMHC, GEO, PRIDE - Download TCGA primary tumour WES/RNA-seq (20 samples) - Download BrainMetShare brain metastasis WES/RNA-seq (20 samples) - Download GTEx normal brain expression, AFND HLA frequencies, IEDB self-antigens - Record sample metadata (source, cancer type, ancestry, treatment history)
Stage 2 - Data Preparation and Labelling - Label positive samples (MS-confirmed peptides) - Generate negative samples (matched length + AA composition) - Remove overlap between positive and negative sets - Split 70/15/15 train/validation/test with batch-aware splitting - Handle missing values, normalise features
Stage 3 - Feature Engineering (23 Features) - NetMHCpan %Ra