ArticleNature materials2019
Avoiding common pitfalls in machine learning omic data science.
Article in Nature materials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 2 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
56 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes.Genome medicine · 2023Pooled it
- Machine learning for genetic prediction of psychiatric disorders: a systematic review.Molecular psychiatry · 2021Pooled it
- Remnant cholesterol inflammation index (RCII) as a potential biomarker for ACPA-negative rheumatoid arthritis: evidence from dual-cohort analysis and serum lipidomics.Clinical rheumatology · 2026Article
- Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026Review
- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.Scientific data · 2026Article
- Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.Annual review of statistics and its application · 2026Article
- Leakage-aware machine learning reveals structured clinical and vaginal microbiome patterns associated with preterm birth in a Mexican cohort.Frontiers in global women's health · 2026Article
- Best Practices for Machine Learning-Assisted Protein Engineering.Journal of chemical information and modeling · 2025Review
- Ten essential tips for robust statistics in cell biology.Nature cell biology · 2025Article
- Article
- Epigenetic clocks and inflammaging: pitfalls caused by ignoring cell-type heterogeneity.GeroScience · 2025Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer.Genome medicine · 2025Article
- Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.Scientific reports · 2025Article
- Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution.Aging · 2024Article
- PredictingmSystems · 2024Article
- Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.PLoS computational biology · 2024Article
- A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts.The Lancet. Digital health · 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
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
30478452What 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.