ReviewNature reviews. Molecular cell biology2022
A guide to machine learning for biologists.
Review in Nature reviews. Molecular cell biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,028 papers, 4 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
1,028 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.Pediatric surgery international · 2026Pooled it
- Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.Langenbeck's archives of surgery · 2026Pooled it
- Machine learning model predicts acute kidney injury in pediatric patients after cardiac surgery: a systematic review and meta-analysis.BMC nephrology · 2026Pooled it
- Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease.Annals of medicine · 2026Observational
- An interpretable machine learning model for predicting febrile seizures following enterovirus infection in children.Annals of medicine · 2026Article
- Habitat Radiomics and Machine Learning for Preoperative Three-class Classification of Pediatric Acute Appendicitis: A Dual-center Study with External Validation.Journal of imaging informatics in medicine · 2026Article
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Identification of novel lncRNAs potentially associated with diabetic retinopathy based on machine learning prediction.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Identification and Experimental Validation of PANoptosis Key Genes for Constructing a PANoptosis Risk Diagnostic Model in Intervertebral Disc Degeneration.The journal of gene medicine · 2026Article
- High-fat diet score reveals total and cardiovascular mortality and identifies protein biomarkers in large cohorts.Journal of advanced research · 2026Article
- Hearing and Cognitive Function in Aged Adults: A Machine Learning and Mediation Analysis of NHANES.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- HPRT1 drives mitochondrial metabolic reprogramming and immunosuppressive microenvironment in oral squamous cell carcinoma.Apoptosis : an international journal on programmed cell death · 2026Article
- The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.Microorganisms · 2026Review
- Dissecting Fluctuating Selection: A Unified Population and Quantitative Genetics Framework.Genome biology and evolution · 2026Article
- High-resolution profiling of bacterial transcriptomes by SEnd-seq.The Biochemical journal · 2026Review
- Machine learning-based integration identifies a 10-gene predictive signature and its classification patterns in schizophrenia.European archives of psychiatry and clinical neuroscience · 2026Article
- Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets.Interdisciplinary sciences, computational life sciences · 2026Article
968 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
Indexed as
Identifiers
34518686What 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.