ReviewFrontiers in bioinformatics2022
Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.
Review in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- Exploring graph-based models for predicting active compounds against triple-negative breast cancer.Molecular diversity · 2026Article
- Opening the black box: insights into ubiquitin-mediated control of innate antiviral immunity and AI-enhanced therapeutics.Frontiers in immunology · 2026Review
- Radiotherapy as a partner for immunotherapy in pancreatic cancer: current landscape and future directions.Frontiers in oncology · 2026Review
- CellHit: a web server to predict and analyze cancer patients' drug responsiveness.Nucleic acids research · 2025Article
- Learning and actioning general principles of cancer cell drug sensitivity.Nature communications · 2025Article
- Article
- Article
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- Precision oncology: a review to assess interpretability in several explainable methods.Briefings in bioinformatics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action. This puts a limitation on their clinical implementation. To address this concern, many recent studies attempt to overcome these issues by developing interpretable deep learning methods that facilitate the understanding of the logic behind the drug response prediction. In this review, we discuss strengths and limitations of recent approaches, and suggest future directions that could guide further improvement of interpretable deep learning in drug sensitivity prediction in cancer research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.