ArticleBioinformatics and biology insights2021
Performance of Regression Models as a Function of Experiment Noise.
Article in Bioinformatics and biology insights, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Assessing the impact of data harmonization on human liver microsomal stability prediction model performance.Results in chemistry · 2026Article
- Machine learning assisted intelligent identification strategy for adulteration peanut oil based on fatty acid GC fingerprint.Food chemistry: X · 2026Article
- Plant photosynthesis in basil (C3) and maize (C4) under different light conditions as basis of an AI-based model for PAM fluorescence/gas-exchange correlation.Frontiers in plant science · 2025Article
- Predicting gestational diabetes mellitus risk at 11-13 weeks' gestation: the role of extrachromosomal circular DNA.Cardiovascular diabetology · 2024Article
- Learning deep representations of enzyme thermal adaptation.Protein science : a publication of the Protein Society · 2022Article
- G4Boost: a machine learning-based tool for quadruplex identification and stability prediction.BMC bioinformatics · 2022Article
- Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.Journal of cheminformatics · 2021Article
- Machine learning for enzyme engineering, selection and design.Protein engineering, design & selection : PEDS · 2021Review
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.Nature communications · 2020Article
- Predicting RNA SHAPE scores with deep learning.RNA biology · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on the test dataset, or whether further model improvement is possible. In biology, this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the metrics of performance attainable by regression models on the test dataset.
resultsWe address this challenge by deriving an expected upper bound for the coefficient of determination (
conclusionsThe new method for estimating upper bounds for model performance on test data should aid researchers in developing ML regression models that reach their maximum potential. Although we study biological datasets in this work, the new upper bound estimates will hold true for regression models from any research field or application area where response variables have associated noise.
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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.