Predicting drug response using omics data and artificial intelligence approach in cancer

Cancer treatments often yield varied responses among patients, highlighting the urgent need for personalized therapeutic approaches. Leveraging recent advances in artificial intelligence (AI) and systems biology, this study introduces a novel AI-driven framework designed to predict how different cancer cell lines respond to specific drugs. Using a robust dataset comprising approximately ۵,۶۲۷,۰۷۲ data points —including cell line, genomic, transcriptomic, proteomic, and mutation variations sourced from COSMIC, GDSC, and Ensembl databases — we focused on the comprehensive analysis of ۳,۰۰۸ crucial genes across ۹۳۳ unique cancer cell lines. Our approach employs a fuzzy vector-based method to construct a gene embedding of dimensions (۱, ۳۰۰۸) for each cell line, where each element represents a fuzzy value calculated based on the extensive dataset. This fuzzy value indicates the relative importance of gene mutations, providing a more nuanced representation of mutation impacts on drug sensitivity than traditional binary embeddings. To model drug data, we utilized Morgan fingerprints, enabling detailed molecular characterization. By integrating multi-omics (genomic, transcriptomic, and proteomic) data, we accurately modeled each gene’s contribution, reflecting the complexity of molecular interactions and their biological significance. These refined representations were fed into artificial neural networks (ANNs) to predict therapeutic responses to targeted treatments. The results demonstrate that this gene importance-based method, combined with deep learning techniques, achieves a Pearson correlation of ۸۱.۱۲%, which is slightly better than recent work in the field. This work marks a significant step forward in precision oncology, offering a robust, data-driven strategy that not only achieves high predictive accuracy but also elucidates the underlying biological factors influencing drug effectiveness. Ultimately, our framework advances the promise of personalized medicine, tailoring cancer treatments to each patient’s unique molecular profile, thereby improving therapeutic efficacy and minimizing adverse effects.

کلیدواژه ها:

precision oncology ، personalized medicine ، neural networks ، drug response prediction