ReviewJournal of proteome research2022
How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning.
Review in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- GPX4 in the Tumor Microenvironment: Not Just Inhibiting Ferroptosis, but Immuno-Metabolic Regulation.Biomolecules · 2026Review
- Research Progress in Multi-Omics Analysis of Dairy Products: Nutritional Quality, Safety Evaluation, and Health Functions.Foods (Basel, Switzerland) · 2026Review
- Feature Down-Selection to Improve Supervised Classification by Machine Learning on Mass Spectrometry Imaging Data.Molecules (Basel, Switzerland) · 2026Article
- Deeper is not always better in plasma proteomics.Nature biotechnology · 2026Article
- Classification with Missing Data - AbioRxiv : the preprint server for biology · 2026Article
- Lipidomics in Children: Noninvasive Sebum Sampling in Children and Adults Allows for Assessment of Lipidomic Differences According to Age, Sex, and Biological Relatedness.Analytical chemistry · 2026Article
- Mass spectrometry-based proteomics of FFPE tissues: progress, limitations, and clinical translation barriers.Clinical proteomics · 2025Review
- Potential of Proteomics in Forensic Phenotyping: A Focus on Biological Sex Estimation.Journal of proteome research · 2025Article
- Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment.Nature communications · 2025Article
- Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.Journal of proteome research · 2025Review
- Latest clinical frontiers related to autism diagnostic strategies.Cell reports. Medicine · 2025Review
- Groomed Fingerprint Sebum Sampling: Reproducibility and Variability According to Anatomical Collection Region and Biological Sex.Molecules (Basel, Switzerland) · 2025Article
- Perspectives in computational mass spectrometry: recent developments and key challenges.Bioinformatics advances · 2025Article
- Metabolomics profiling identifies diagnostic metabolic signatures for pregnancy loss: a cross-sectional study from northwestern China.Frontiers in endocrinology · 2025Article
- Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics.Angewandte Chemie (International ed. in English) · 2024Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- Skin Surface Sebum Analysis by ESI-MS.Biomolecules · 2024Review
- Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.Clinical proteomics · 2024Article
- Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients.Clinical and experimental immunology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
This review "teaches" researchers how to make their lackluster proteomics data look really impressive, by applying an inappropriate but pervasive strategy that selects features in a biased manner. The strategy is demonstrated and used to build a classification model with an accuracy of 92% and AUC of 0.98, while relying completely on random numbers for the data set. This "lesson" in data processing is not to be practiced by anyone; on the contrary, it is meant to be a cautionary tale showing that very unreliable results are obtained when a biomarker panel is generated first, using all the available data, and then tested by cross-validation. Data scientists describe the error committed in this scenario as having test data leak into the feature selection step, and it is currently a common mistake in proteomics biomarker studies that rely on machine learning. After the demonstration, advice is provided about how machine learning methods can be applied to proteomics data sets without generating artificially inflated accuracies.
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.