ArticleScientific reports2019
Towards perturbation prediction of biological networks using deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mathematical models of intercellular signaling in breast cancer.Seminars in cancer biology · 2025Review
- Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide.Frontiers in cell and developmental biology · 2024Review
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation.Patterns (New York, N.Y.) · 2022Article
- Machine learning methods trained on simple models can predict critical transitions in complex natural systems.Royal Society open science · 2022Article
- A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries.Frontiers in public health · 2022Article
- Group Behavior and Emergence of Cancer Drug Resistance.Trends in cancer · 2021Review
- Systems biology approach suggests new miRNAs as phenotypic stability factors in the epithelial-mesenchymal transition.Journal of the Royal Society, Interface · 2020Article
- Network Approaches for Dissecting the Immune System.iScience · 2020Review
- BioModels-15 years of sharing computational models in life science.Nucleic acids research · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mapping of the physical interactions between biochemical entities enables quantitative analysis of dynamic biological living systems. While developing a precise dynamical model on biological entity interaction is still challenging due to the limitation of kinetic parameter detection of the underlying biological system. This challenge promotes the needs of topology-based models to predict biochemical perturbation patterns. Pure topology-based model, however, is limited on the scale and heterogeneity of biological networks. Here we propose a learning based model that adopts graph convolutional networks to learn the implicit perturbation pattern factors and thus enhance the perturbation pattern prediction on the basic topology model. Our experimental studies on 87 biological models show an average of 73% accuracy on perturbation pattern prediction and outperforms the best topology-based model by 7%, indicating that the graph-driven neural network model is robust and beneficial for accurate prediction of the perturbation spread modeling and giving an inspiration of the implementation of the deep neural networks on biological network modeling.
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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.