ArticleCancer cell2020
Biologically Informed Neural Networks Predict Drug Responses.
Article in Cancer cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 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
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations.Heliyon · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.Frontiers in radiology · 2024Review
Corrections and comments
- Comment on
Authors and funding
2 authors.
Funding
Abstract
Deep neural networks often achieve high predictive accuracy on biological problems, but it can be hard to contextualize how and explain why predictions are made. In this issue, Kuenzi et al. model the sensitivity of cancers to drugs using deep neural networks with a hierarchical structure derived from the Gene Ontology.
Indexed as
Identifiers
33096022What Socratic 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 Socratic graph.