ArticleCurrent genomics2016
Constructing Networks of Organelle Functional Modules in Arabidopsis.
Article in Current genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Approaches in Gene Coexpression Analysis in Eukaryotes.Biology · 2022Review
- Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning.Frontiers in genetics · 2021Article
- Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships.Frontiers in plant science · 2020Article
- Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana.Planta · 2019Article
- DisSetSim: an online system for calculating similarity between disease sets.Journal of biomedical semantics · 2017Article
- Multiple kernels learning-based biological entity relationship extraction method.Journal of biomedical semantics · 2017Article
- A novel method to identify pre-microRNA in various species knowledge base on various species.Journal of biomedical semantics · 2017Article
- Predicting disease-related genes using integrated biomedical networks.BMC genomics · 2017Article
- An improved method for functional similarity analysis of genes based on Gene Ontology.BMC systems biology · 2016Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid accumulation of gene expression data, gene functional module identification has become a widely used approach in functional analysis. However, tools to identify organelle functional modules and analyze their relationships are still missing. We present a soft thresholding approach to construct networks of functional modules using gene expression datasets, in which nodes are strongly co-expressed genes that encode proteins residing in the same subcellular localization, and links represent strong inter-module connections. Our algorithm has three steps. First, we identify functional modules by analyzing gene expression data. Next, we use a self-adaptive approach to construct a mixed network of functional modules and genes. Finally, we link functional modules that are tightly connected in the mixed network. Analysis of experimental data from Arabidopsis demonstrates that our approach is effective in improving the interpretability of high-throughput transcriptomic data and inferring function of unknown genes.
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