What is Predictive Models

This research endeavors to revolutionize precision oncology by leveraging deep learning methodologies to unravel dominant amino acid sequences in cancer patients. The study aims to identify and characterize these sequences, elucidating their potential as biomarkers and targets for personalized therapeutic interventions. By integrating genomic and proteomic data through advanced deep learning algorithms, the research seeks to enhance our understanding of the molecular underpinnings of cancer, ultimately paving the way for more effective and tailored treatments. The primary objectives include employing high-throughput sequencing technologies and mass spectrometry for comprehensive genomic and proteomic profiling of cancer tissues. Deep learning models will be applied to analyze the resulting multi-omics data, with a focus on identifying dominating amino acid sequences across diverse cancer types. The research will also investigate the association between these sequences and clinical parameters, such as tumor stage, treatment response, and patient outcomes.