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Drug Synergy Prediction on Diverse Cancer Cell-Lines Using Deep Learning

Labbaf, Farzaneh | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56041 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Hossein Khalaj, Babak
  7. Abstract:
  8. Despite significant progress in cancer treatment, drug resistance remains a major challenge. Synergistic drug combinations offer a promising approach to overcome drug resistance and reduce side effects. Still, despite high-throughput testing technologies, existing drug combination databases suffer from biases and a lack of diversity in tested cancer cell lines, which challenges the prediction of drug response on novel cell targets. To address this critical need, we designed a two-level deep learning method that uses large-scale gene expression datasets to estimate the score and synergy of drug compounds on a wide variety of cancer cell lines. Our model includes an auto-encoder that train on a large and diverse dataset of cancer cell lines to create concise and comprehensive representations of each cell line, which are used to predict the synergy of combination drugs. Our approach has shown better results than base-line model in predicting drug synergy on diverse cells as well as unseen cells. Furthermore, our model has demonstrated the ability to generalize to different cell types as well as unseen cell lines on Almanac and Dragcomb data. By incorporating gene expression information, our model provides accurate and reliable predictions for combination drug response, paving the way for more effective combination and personalized cancer therapies
  9. Keywords:
  10. Machine Learning ; Drug Sensitivity ; Gene Expression Data ; Deep Learning ; Pharmaceutical Compound ; Drug Synergy ; Combination Therapy ; Cancer Treatment

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