ArticleCommunications biology2021
Systematic auditing is essential to debiasing machine learning in biology.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed.
- From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Why do AI projects fail in drug development and pharma? Insights from multi-year Pistoia Alliance studies.Journal of computer-aided molecular design · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- AI-driven breath biopsy from a case-control study assists in the early detection of paediatric brain tumours.EBioMedicine · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill.The Lancet. Respiratory medicine · 2025Review
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review.NPJ digital medicine · 2024Review
- Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning.Bioinformatics advances · 2024Article
- Advancing age grading techniques forBiology methods & protocols · 2024Article
- Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.Theranostics · 2024Article
- Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2024Article
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- Enabling interpretable machine learning for biological data with reliability scores.PLoS computational biology · 2023Article
- Challenges in translational machine learning.Human genetics · 2022Review
- Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics.Patterns (New York, N.Y.) · 2022Review
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
- Modeling in systems biology: Causal understanding before prediction?Patterns (New York, N.Y.) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences. We use this auditing framework to examine biases in three ML applications of therapeutic interest and identify unrecognized biases that hinder the ML process and result in substantially reduced model performance on new datasets. Ultimately, we show that ML models tend to learn primarily from data biases when there is insufficient signal in the data to learn from. We provide detailed protocols, guidelines, and examples of code to enable tailoring of the auditing framework to other biomedical applications.
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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.