ArticleBMC bioinformatics2007
Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Gated Graph Attention Network for Cancer Prediction.Sensors (Basel, Switzerland) · 2021Article
- Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.Advances in bioinformatics · 2016Article
- Inferring cell cycle feedback regulation from gene expression data.Journal of biomedical informatics · 2011Article
- Stochastic spatio-temporal dynamic model for gene/protein interaction network in early Drosophila development.Gene regulation and systems biology · 2009Article
- Reverse engineering module networks by PSO-RNN hybrid modeling.BMC genomics · 2009Article
- Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets.BMC systems biology · 2008Article
- Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data.BMC bioinformatics · 2008Article
- Underlying principles of natural selection in network evolution: systems biology approach.Evolutionary bioinformatics online · 2007Article
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Authors and funding
2 authors.
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Abstract
backgroundModeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new diagnose and therapies. Several mathematical models have been used to explore the phenomena of transcriptional regulatory mechanisms in Saccharomyces cerevisiae. However, the contemplating on controlling of feed-forward and feedback loops in transcriptional regulatory mechanisms is not resolved adequately in Saccharomyces cerevisiae, nor is in human cancer cells.
resultsIn this study, we introduce a Genetic Algorithm-Recurrent Neural Network (GA-RNN) hybrid method for finding feed-forward regulated genes when given some transcription factors to construct cancer-related regulatory modules in human cancer microarray data. This hybrid approach focuses on the construction of various kinds of regulatory modules, that is, Recurrent Neural Network has the capability of controlling feed-forward and feedback loops in regulatory modules and Genetic Algorithms provide the ability of global searching of common regulated genes. This approach unravels new feed-forward connections in regulatory models by modified multi-layer RNN architectures. We also validate our approach by demonstrating that the connections in our cancer-related regulatory modules have been most identified and verified by previously-published biological documents.
conclusionThe major contribution provided by this approach is regarding the chain influences upon a set of genes sequentially. In addition, this inverse modeling correctly identifies known oncogenes and their interaction genes in a purely data-driven way.
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