Processant ...

# Background & Motivation High-throughput analytical techniques such as mass spectrometry generates vast amounts of spectral data (MS and MS/MS). The interpretation of these complex signals is critical for applications in metabolomics, pharmaceuticals, and healthcare. However, current machine learning (ML) models for spectral annotation face challenges due to the high dimensionality, sparsity, and noise in MS/MS signals. Efficient preprocessing, feature extraction, and big data infrastructure are essential for scalable and accurate metabolite identification. This project integrates telecommunications engineering principles, particularly in signal processing, instrumentation, and high-performance computing, to improve deep learning models for spectral annotation. # Objective This TFG project aims to enhance ChemEmbed (https://www.biorxiv.org/content/10.1101/2025.02.07.637102v1), a deep learning framework that models MS/MS spectral data and chemical structures. The focus will be on: - Optimizing signal processing techniques for spectral data preprocessing and feature extraction. - Improving computational infrastructure for handling large-scale MS/MS datasets efficiently. - Enhancing deep learning models for better spectral pattern recognition and metabolite annotation. # Methodology 1. Signal processing & Data preprocessing - Apply advanced filtering techniques (e.g., wavelet transforms, Fourier analysis) to reduce noise and enhance signal quality. - Optimize spectral binning strategies and intensity transformations for improved feature extraction. 2. Big Data & High-performance computing for MS/MS analysis - Implement parallel computing techniques to accelerate deep learning model training on large-scale MS/MS datasets. - Optimize data storage and retrieval using cloud-based architectures and distributed databases. - Develop an efficient API infrastructure to facilitate seamless communication between spectrometry instruments and computational servers. 3. Deep learning model optimization - Experiment with convolutional neural networks (CNNs) for spectral feature extraction. - Optimize model architectures and explore alternative loss functions for improved annotation accuracy. - Validate performance using benchmark spectral datasets, evaluating classification accuracy, signal correlation metrics, and computational efficiency. #Expected Outcomes: - Development of optimized signal processing techniques for MS/MS spectral analysis. - Improved hardware-software integration for real-time spectral data acquisition and analysis. - Enhanced big data infrastructure for handling large-scale metabolomics datasets. - More accurate and scalable metabolite annotation using deep learning models. # Milestones: - Literature review & signal processing techniques - Model development & experimentation - Performance evaluation & optimization - Documentation & final report This project bridges telecommunications engineering, biomedical signal processing, and AI-driven metabolomics, offering hands-on experience with instrumentation data, big data computing, and deep learning for spectral analysis.