ArticleGigaByte (Hong Kong, China)2024
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models.
Article in GigaByte (Hong Kong, China), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed, 22 citations in OpenAlex.
- Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group.Research square · 2026Article
- Exposome-wide patterns predict brain health in aging.Nature communications · 2026Article
- Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data.Brain structure & function · 2026Article
- Individual identifiability following Procrustes alignment of functional gradients: effect of subspace dimensionality.Communications biology · 2026Article
- Can we predict sleep health based on brain features? A large-scale machine learning study using the UK Biobank.Brain communications · 2026Article
- Impact of tractogram filtering and graph creation for structural connectomics in subjects with Parkinson's disease.Frontiers in human neuroscience · 2026Article
- White Matter Bundle Reconstruction From Single-Shell Diffusion Magnetic Resonance Imaging: Test-Retest Reliability and Predictive Capability Across Orientation Distribution Function Reconstruction Methods.Human brain mapping · 2025Article
- Pitfalls in using ML to predict cognitive function performance.Scientific reports · 2025Article
- Voxel-Wise or Region-Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?Human brain mapping · 2025Article
- Effective workflow from multimodal MRI data to model-based prediction.Scientific reports · 2025Article
- Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.Human brain mapping · 2025Article
- Variations of autonomic arousal mediate the reportability of mind blanking occurrences.Scientific reports · 2025Article
- Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment.Brain : a journal of neurology · 2024Article
- Network and state specificity in connectivity-based predictions of individual behavior.Human brain mapping · 2024Article
- Sex classification from functional brain connectivity: Generalization to multiple datasets.Human brain mapping · 2024Article
- Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.medRxiv : the preprint server for health sciences · 2024Article
- Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models.GigaByte (Hong Kong, China) · 2024Article
- Network and State Specificity in Connectivity-Based Predictions of Individual Behavior.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The fast-paced development of machine learning (ML) and its increasing adoption in research challenge researchers without extensive training in ML. In neuroscience, ML can help understand brain-behavior relationships, diagnose diseases and develop biomarkers using data from sources like magnetic resonance imaging and electroencephalography. Primarily, ML builds models to make accurate predictions on unseen data. Researchers evaluate models' performance and generalizability using techniques such as cross-validation (CV). However, choosing a CV scheme and evaluating an ML pipeline is challenging and, if done improperly, can lead to overestimated results and incorrect interpretations. Here, we created julearn, an open-source Python library allowing researchers to design and evaluate complex ML pipelines without encountering common pitfalls. We present the rationale behind julearn's design, its core features, and showcase three examples of previously-published research projects. Julearn simplifies the access to ML providing an easy-to-use environment. With its design, unique features, simple interface, and practical documentation, it poses as a useful Python-based library for research projects.
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.