ReviewComputational and structural biotechnology journal2022
Exploring synergies between plant metabolic modelling and machine learning.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- New insights in metabolism modelling to decipher plant-microbe interactions.The New phytologist · 2025Review
- Non-canonical plant metabolism.Nature plants · 2025Review
- Navigating the challenges of engineering composite specialized metabolite pathways in plants.The Plant journal : for cell and molecular biology · 2025Review
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- diel_models: a python package for systematic integration of day-night cycles into plant genome-scale metabolic models.Bioinformatics advances · 2025Article
- A diel multi-tissue genome-scale metabolic model of Vitis vinifera.PLoS computational biology · 2024Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity ofMetabolites · 2023Article
- Data Science and Plant Metabolomics.Metabolites · 2023Review
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As plants produce an enormous diversity of metabolites to help them adapt to the environment, the study of plant metabolism is of utmost importance to understand different plant phenotypes. Omics data have been generated at an unprecedented rate for several organisms, including plants, and are widely used to study the central dogma of molecular biology, connecting the genome to phenotypes. Constraint-based modelling (CBM) methods, working over genome-scale metabolic models (GSMMs), have been crucial for organising and analysing omics data by integrating them with biochemical knowledge. In 2009, the first plant GSMM was reconstructed and, since then, several advances have been made, including the creation of context- and multi-tissue models that have supported the study of plant metabolism. Nevertheless, plant metabolic modelling remains very challenging. In parallel, as omics datasets are complex and heterogeneous, machine learning (ML) models have been applied in their interpretation to foster knowledge discovery. Recently, the first studies combining both CBM and ML approaches have emerged and have shown promising results. Here, we present the major advances in plant metabolic modelling and review the main CBM-ML hybrid studies. Finally, we discuss the application of machine learning to address the unique challenges of plant metabolic modelling.
Indexed as
Identifiers
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.