ArticlePloS one2012
Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 55 citations in OpenAlex.
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- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- Prediction of plant secondary metabolic pathways using deep transfer learning.BMC bioinformatics · 2023Article
- A novel hybrid framework for metabolic pathways prediction based on the graph attention network.BMC bioinformatics · 2022Article
- Predicting protein network topology clusters from chemical structure using deep learning.Journal of cheminformatics · 2022Article
- MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.Bioinformatics (Oxford, England) · 2022Article
- Predicting biological pathways of chemical compounds with a profile-inspired approach.BMC bioinformatics · 2021Article
- iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network.Computational and mathematical methods in medicine · 2021Article
- Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.Computational and mathematical methods in medicine · 2020Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- Non-small-cell lung cancer pathological subtype-related gene selection and bioinformatics analysis based on gene expression profiles.Molecular and clinical oncology · 2018Article
- Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis.Frontiers in genetics · 2018Article
- Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm.BioMed research international · 2017Article
- Article
- Identification of novel candidate drivers connecting different dysfunctional levels for lung adenocarcinoma using protein-protein interactions and a shortest path approach.Scientific reports · 2016Article
- Identifying novel genes and chemicals related to nasopharyngeal cancer in a heterogeneous network.Scientific reports · 2016Article
- Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities.Molecular genetics and genomics : MGG · 2016Article
- Metabolic pathway predictions for metabolomics: a molecular structure matching approach.Journal of chemical information and modeling · 2015Article
- Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach.Computational and mathematical methods in medicine · 2015Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.
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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.