ArticleHuman genetics2022
Interpretable machine learning for genomics.
Article in Human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.Biochemical genetics · 2026Article
- From GWAS Signals to Molecular Mechanisms: Explainable AI for Causal Gene Prioritization and Biomolecular Target Interpretation.Biomolecules · 2026Review
- An interpretable machine learning framework for dog breed inference and ancestry decomposition.bioRxiv : the preprint server for biology · 2026Article
- Interpretable machine learning for cattle breed classification and SNP prioritization.Genetics, selection, evolution : GSE · 2026Article
- Article
- Machine and Deep Learning Reveal Sequence Determinants Encoding Bivalent Histone Modifications.Communications biology · 2026Article
- A multi-branch ensemble learning framework for detection of non-small cell lung cancer via T-cell receptor sequencing.BMC cancer · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- Explainable AI needs formalization.NPJ artificial intelligence · 2026Review
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Application of Artificial Intelligence Technology in Plant MicroRNA Research: Progress, Challenges, and Prospects.International journal of molecular sciences · 2025Review
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- Deciphering Sequence Determinants of Zygotic Genome Activation Genes: Insights From Machine Learning and the ZGAExplorer Platform.Cell proliferation · 2025Article
- Deep learning-driven proteomics analysis for gene annotation in the renin-angiotensin system.European journal of pharmacology · 2025Article
- Enhancing disease clustering through symptom-based analysis and large language model interpretations.Scientific reports · 2025Article
- Regulating genome language models: navigating policy challenges at the intersection of AI and genetics.Human genetics · 2025Review
- Unmet needs in autoimmune liver diseases.Current opinion in immunology · 2025Review
- Multi-task genomic prediction using gated residual variable selection neural networks.BMC bioinformatics · 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
1 author.
Funding
Abstract
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.
Indexed as
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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.