Protein tandem mass spectrometry data are most often interpreted by matching observed
mass spectra to a protein database derived from the reference genome of the sample being …

In computational proteomics, machine learning (ML) has emerged as a vital tool for
enhancing data analysis. Despite significant advancements, the diversity of ML model …

Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics.
Unlike traditional database searches, deep learning excels at de novo peptide sequencing …

Training machine learning models for tasks such as de novo sequencing or spectral
clustering requires large collections of confidently identified spectra. Here we describe a …

Abstract Heterogeneous Graph Neural Networks (HGNNs) have demonstrated significant
success in capturing complex interactions within heterogeneous graphs to learn graph …

A core computational challenge in the analysis of mass spectrometry data is the de novo
sequencing problem, in which the generating amino acid sequence is inferred directly from …

Transformers, the backbone of modern large language models (LLMs), face inherent
architectural limitations that impede their reasoning capabilities. Unlike recurrent networks …

Major Histocompatibility Complex (MHC) molecules play a critical role in the immune system
by presenting peptides on the cell surface for recognition by T-cells. Tumor cells often …

Peptide sequencing via tandem mass spectrometry (MS/MS) is fundamental in proteomics
data analysis, playing a pivotal role in unraveling the complex world of proteins within …

This thesis explores deep learning methods for protein identification and property prediction,
encompassing two primary areas: mass spectrometry-based protein sequence identification …