Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Clicks: 81
ID: 282824
2022
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
24.0
/100
81 views
24 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #262 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Cancer claims millions of lives yearly worldwide. While many therapies have
been made available in recent years, by in large cancer remains unsolved.
Exploiting computational predictive models to study and treat cancer holds
great promise in improving drug development and personalized design of
treatment plans, ultimately suppressing tumors, alleviating suffering, and
prolonging lives of patients. A wave of recent papers demonstrates promising
results in predicting cancer response to drug treatments while utilizing deep
learning methods. These papers investigate diverse data representations, neural
network architectures, learning methodologies, and evaluations schemes.
However, deciphering promising predominant and emerging trends is difficult due
to the variety of explored methods and lack of standardized framework for
comparing drug response prediction models. To obtain a comprehensive landscape
of deep learning methods, we conducted an extensive search and analysis of deep
learning models that predict the response to single drug treatments. A total of
60 deep learning-based models have been curated and summary plots were
generated. Based on the analysis, observable patterns and prevalence of methods
have been revealed. This review allows to better understand the current state
of the field and identify major challenges and promising solution paths.
| Reference Key |
stevens2022deep
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Alexander Partin; Thomas S. Brettin; Yitan Zhu; Oleksandr Narykov; Austin Clyde; Jamie Overbeek; Rick L. Stevens |
| Journal | arXiv |
| Year | 2022 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.