ArticleFrontiers in artificial intelligence2022
Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Autoencoders decode polyunsaturated fatty acid metabolism with strong genetic architecture in cancer risk.EBioMedicine · 2026Article
- Unbiased distance correlation with sample-size-aware confidence bounds for comparative omics network analysis.Frontiers in bioinformatics · 2026Article
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Metabolic Fluxes Using Deep Learning Based on Enzyme Variations:International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min
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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.