ReviewPLoS computational biology2019
Machine and deep learning meet genome-scale metabolic modeling.
Review in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.
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
138 citing papers in PubMed.
- Personalized whole-body modeling links gut microbiota to metabolic perturbations in Alzheimer's disease.Gut microbes · 2026Article
- Review
- Artificial intelligence and peripheral neuropathies: Strategies for the development, application, and repair of regenerative biomaterials.Neural regeneration research · 2026Article
- Biochemically Constrained Multi-Omics Integration Reveals Protein-Metabolite Dependencies Across Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Systems biology during 20 years of PLoS Computational Biology.PLoS computational biology · 2026Article
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- Rhodo-Box: A Synthetic Biology Toolbox to Facilitate Metabolic Engineering ofACS synthetic biology · 2026Article
- Predicting Strain-Specific Metabolic Capabilities in the GenusComputational and structural biotechnology journal · 2026Article
- The renaissance ofFrontiers in immunology · 2026Review
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers.Frontiers in pharmacology · 2026Article
- GAN-enhanced machine learning and metabolic modeling identify reprogramming in pancreatic cancer.PLoS computational biology · 2026Article
- A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.Frontiers in plant science · 2026Review
- Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression.Frontiers in bioinformatics · 2026Article
- A Machine Learning Model to Reframe the Concept of Shelf-Life in Bakery Products: PDO Sourdough as a Technological Preservation Model.Foods (Basel, Switzerland) · 2025Article
- Neurometabolic predictors of mental effort in the frontal cortex.Translational psychiatry · 2025Article
- From reactants to products: computational methods for biosynthetic pathway design.Synthetic and systems biotechnology · 2025Review
- Artificial intelligence and computational methods in human metabolism research: A comprehensive survey.Journal of pharmaceutical analysis · 2025Review
78 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
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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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.