Deep Learning for predicting synergy effects of drug combinations

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Drug combination therapies have numerous advantages compared to mono therapies and are commonly used for cancer treatment. If the right drugs are combined it is possible to reduce the acquisition of drug resistance, lower the used doses and to achieve a higher efficiency. However, the huge search space of possible combinations as well as the lack of effective experimental procedures make synergy research extremely challenging. Nonetheless, understanding the interactions and effects of drug combinations is crucial for proper treatment.
Original languageEnglish
Title of host publicationNeural Information Processing Systems (NIPS 2016)
Number of pages1
Publication statusPublished - 2016

Fields of science

  • 303 Health Sciences
  • 304 Medical Biotechnology
  • 304003 Genetic engineering
  • 305 Other Human Medicine, Health Sciences
  • 101004 Biomathematics
  • 101018 Statistics
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102004 Bioinformatics
  • 102010 Database systems
  • 102015 Information systems
  • 102019 Machine learning
  • 106023 Molecular biology
  • 106002 Biochemistry
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 106041 Structural biology
  • 301 Medical-Theoretical Sciences, Pharmacy
  • 302 Clinical Medicine

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Nano-, Bio- and Polymer-Systems: From Structure to Function
  • Medical Sciences (in general)
  • Health System Research
  • Clinical Research on Aging

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