ReviewThe AAPS journal2020
Challenge-Enabled Machine Learning to Drug-Response Prediction.
Review in The AAPS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses 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
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- The predictive value of machine learning and nomograms for lymph node metastasis of prostate cancer: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2023Pooled it
- Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Computer-Aided Drug Design and Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Health Management of Type 2 Diabetes Mellitus and Its Complications: A Machine Learning Algorithm-Based Retrospective Study in Chinese Communities.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Utility of Machine Learning Models to Predict Lymph Node Metastasis of Japanese Localized Prostate Cancer.Cancers · 2024Article
- Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer.Journal of cancer research and clinical oncology · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer.Frontiers in medicine · 2021Article
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
Abstract
In recent decades, the advancement of computational algorithms and the availability of big data have enabled artificial intelligence (AI) to dramatically improve predictive performance in nearly all research areas. Specifically, machine learning (ML) techniques, a major branch of AI, have been widely used in many tasks of drug discovery and development, including predicting treatment effects, identifying target genes and functional pathways, as well as selecting potential biomarkers. However, in practice, blindly applying ML methods may lead to common pitfalls, including overfitting and lack of generalizability. Therefore, how to improve the robustness and prediction accuracy of ML methods has become a crucial problem for researchers. In this review, we summarize the application of ML models to drug discovery by introducing the top-performing methods developed from large-scale drug-related data challenges in recent years.
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.