ReviewHuman genetics2022
What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.
Review in Human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.Molecular biotechnology · 2025Pooled it
- Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning.International dental journal · 2026Article
- Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.Intensive care medicine experimental · 2026Review
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- Interpretable machine learning models based on CT radiomics for predicting chemoradiotherapy response in rectal cancer.BMC medical imaging · 2026Article
- Ensemble learning for air quality index prediction: integrating gradient boosting, XGBoost, and stacking with SHAP-based interpretability.Scientific reports · 2026Article
- Revisiting AI Interpretability in Precision Oncology: Why Predictive Accuracy Does Not Ensure Stable Feature Importance.Cancers · 2026Article
- Prediction of Ascending Aortic Dilation and Analysis of Influencing Factors in Bicuspid Aortic Valve Patients Using an Explainable Machine Learning Model.Cardiology research and practice · 2026Article
- Article
- Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients.Frontiers in oncology · 2026Article
- Assessing and optimizing Team-Based Learning in undergraduate cosmetic dermatology education: an empirical study using interpretable machine learning.BMC medical education · 2025Article
- Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM.Annals of medicine · 2025Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios.BMC endocrine disorders · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- Classification of AD and bvFTD using neuropsychological and neuropsychiatric variables: a machine learning study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Enhancing Alzheimer's Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
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
Abstract
Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are commonly used in genetics to predict or classify subjects, but some methods evaluate which features (variables) are responsible for creating a good prediction; this is called feature importance. This is critical in genetics, as researchers are often interested in which features (e.g., SNP genotype or environmental exposure) are responsible for a good prediction. This allows for the deeper analysis beyond simple prediction, including the determination of risk factors associated with a given phenotype. Feature importance further permits the researcher to peer inside the black box of many ML algorithms to see how they work and which features are critical in informing a good prediction. This review focuses on ML methods that provide feature importance metrics for the analysis of genetic data. Five major categories of ML algorithms: k nearest neighbors, artificial neural networks, deep learning, support vector machines, and random forests are described. The review ends with a discussion of how to choose the best machine for a data set. This review will be particularly useful for genetic researchers looking to use ML methods to answer questions beyond basic prediction and classification.
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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.