ArticleProceedings of the National Academy of Sciences of the United States of America2020
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy.Nature cell biology · 2026Article
- Yeast as a Model for Human Disease.International journal of molecular sciences · 2026Review
- GAN-enhanced machine learning and metabolic modeling identify reprogramming in pancreatic cancer.PLoS computational biology · 2026Article
- FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets.Bioinformatics (Oxford, England) · 2025Article
- Yeast adapts to diverse ecological niches driven by genomics and metabolic reprogramming.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning reveals genes impacting oxidative stress resistance across yeasts.Nature communications · 2025Article
- Fermentation design and process optimization strategy based on machine learning.Biodesign research · 2025Review
- Evolution and applications of genome-scale metabolic models in yeast systems biology studies.FEMS yeast research · 2025Review
- Integrating yeast biodiversity and machine learning for predictive metabolic engineering.FEMS yeast research · 2025Review
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
- A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.Molecular systems biology · 2024Article
- Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.Cell reports methods · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources.Molecular systems biology · 2024Article
- Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.Synthetic and systems biotechnology · 2024Article
- From beer to breadboards: yeast as a force for biological innovation.Genome biology · 2024Review
- FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143
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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.