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DAPM-CDR: A domain adaptation prompting model for drug response prediction

Future Generation Computer Systems, ISSN: 0167-739X, Vol: 160, Page: 316-324
2024
  • 0
    Citations
  • 0
    Usage
  • 10
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Captures
    10
  • Mentions
    1
    • News Mentions
      1
      • 1

Most Recent News

Findings from Northeast Forestry University in Cancer Reported (Dapm-cdr: a Domain Adaptation Prompting Model for Drug Response Prediction)

2024 OCT 31 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Daily -- New research on Cancer is the subject of a report.

Article Description

The rising incidence and mortality rates of cancer present significant challenges to global health. Variations in tumor growth rates and treatment responses have revealed the limitations of traditional therapies, highlighting the urgent need for predictive models for cancer drug responses based on computational methods. Current drug response prediction methods often fall short in accurately predicting responses for rare cancers with limited data. To address these issues, we propose a domain adaptation prompting model for drug response prediction, DAPM-CDR. DAPM-CDR is trained with cancer types with rich response data of cancer cell lines, integrating both common and specific information into prompts through contrastive learning, to transfer knowledge to target tasks that with sparse data on cancer cell line responses, thereby enhancing the generalization capabilities across various cancer types. The experiment results demonstrate that DAPM-CDR outperforms several competitive methods in predicting drug responses of cell lines, particularly excelling with data from rare cancers and showing significant performance enhancements.

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