ReviewFrontiers in molecular biosciences2022
Interpretable machine learning methods for predictions in systems biology from omics data.
Review in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging.NPJ systems biology and applications · 2025Trial
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Network propagation in bipartite metabolite-reaction graphs for metabolomic data exploration.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- MuSpAn: a toolbox for multiscale spatial analysis.Nature communications · 2026Article
- Radon-Induced Radiation Biomarkers: A Scoping Review from Exposure Dosimetry to Early Biological Effects on the Lung.International journal of molecular sciences · 2026Article
- From triangle to pyramid: Understanding host-pathogen-microniome-environment interplay for sustainable, enviromics-empowered management of plant diseases.Plant communications · 2026Review
- Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.Journal of agricultural and food chemistry · 2026Article
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- Interpretation of multivariate metabolomic models through network-guided perturbation-based explanations.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
- ATF6 activation alters colonic lipid metabolism causing tumour-associated microbial adaptation.Nature metabolism · 2025Article
- Advances in Functional Genomics for Exploring Abiotic Stress Tolerance Mechanisms in Cereals.Plants (Basel, Switzerland) · 2025Review
- Modeling the Interactions Between Chemicals and Proteins to Predict the Health Consequences of Air Pollution.International journal of environmental research and public health · 2025Article
- Sequence-Only Prediction of Super-Enhancers in Human Cell Lines Using Transformer Models.Biology · 2025Article
- Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.Journal of chemical theory and computation · 2025Article
- Article
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- Article
- The impact of dietary fiber on colorectal cancer patients based on machine learning.Frontiers in nutrition · 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
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by "omics" experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design "interpretable" models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: "What is interpretability?" We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.
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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.