Deep Learning for Proteomics
Cortexa Genomix harnesses the power of deep neural networks to extract meaningful patterns from high-dimensional mass spectrometry data. We recognize that subtle differences in proteomic and metabolomic profiles can be indicative of critical biological states, from normal processes to cancerous abnormalities. Our expertise lies in designing and training sophisticated models, such as 1D-Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to classify these complex spectral signatures with high accuracy.
We implement a comprehensive workflow that includes data compression and feature selection using techniques like χ² analysis to prepare spectra for efficient model training. By treating mass spectra as sequential data, our LSTM models can learn the interdependencies between spectral features, leading to superior classification performance. Our work with prostate cancer data demonstrates our ability to achieve outstanding accuracy (95.4%), providing a reliable computational backbone for diagnostic and research applications.
Confusion Matrix for Breat Cancer Prediction using our LSTM model, achieving 95.4% accuracy on mass spectrum classification.