ReviewBioData mining2017
Ten quick tips for machine learning in computational biology.
Review in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 246 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
246 citing papers in PubMed, 1 synthesis or guideline pooled it, 905 citations in OpenAlex.
- Practical guidelines for validation of supervised machine learning models in accelerometer-based animal behaviour classification.The Journal of animal ecology · 2025Pooled it
- Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction.BMC gastroenterology · 2024Trial
- Gene expression signatures predict response to therapy with growth hormone.The pharmacogenomics journal · 2021Trial
- Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program.BMJ open diabetes research & care · 2021Trial
- Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics-Based Optimization with Limited Activity Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Firemaster 550 differentially alters gene expression underlying synaptic function in amygdala of prairie voles after gestational or lactational exposure.Neurotoxicology · 2026Article
- QSAR, molecular dynamics, and biological evaluation of novel myeloperoxidase inhibitors via ligand-based pharmacophore modeling as potential anticancer agents.Journal of computer-aided molecular design · 2026Article
- Application of Supervised Machine Learning Algorithms to Morphological Classification of Bufo bufo (Linnaeus, 1758) and B. verrucosissimus (Pallas, 1814) From Türkiye.Journal of morphology · 2026Article
- Refining Accelerometer-Based Animal Behaviour Classifications With Sequence-Informed Post-Processing.Ecology and evolution · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Data-driven exploration of electronic nose technology to differentiate bacteria in blood cultures under biofilm-promoting conditions.Scientific reports · 2026Article
- Investigating a deterministic canonical model for tropical influenza.Scientific reports · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- Evaluation of panoramic radiography for artificial intelligence-based assessment of impacted maxillary canines using cone-beam computed tomography as reference.BMC oral health · 2026Article
- BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.Computational and structural biotechnology journal · 2026Article
- Machine Learning-Based Prediction of Three-Year Heart Failure and Mortality After Premature Ventricular Contraction Ablation.Diagnostics (Basel, Switzerland) · 2025Article
- A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors.Cell reports methods · 2025Article
- Statistical consideration in nephrology research.Kidney research and clinical practice · 2025Article
- Exploring the relative importance of the factors associated with menopausal symptoms using a random forest model: a cross-sectional study.Women's health nursing (Seoul, Korea) · 2025Article
- The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review.Animals : an open access journal from MDPI · 2025Review
186 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
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.
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.