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Abstract MotivationProtein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties. ResultsGLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study. Availability and implementationAll code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER. Supplementary informationSupplementary data are available at Bioinformatics online.
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This content will become publicly available on May 19, 2026
An Introduction to Network Analysis in Plant Biology
This beginner’s guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant -omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype-phenotype relationships. In this paper, we introduce networks and compare network-building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED-II, and STRING. AraNet, and GeneMANIA are functional association networks, ATTED-II is a gene coexpression database, and STRING is a protein-protein interaction database. AraNet, and ATTED-II are plant-specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks for Arabidopsis thaliana and non-plant species, and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of the A. thaliana Nuclear Factor-Y (NF-Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.
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- Award ID(s):
- 2048410
- PAR ID:
- 10637943
- Publisher / Repository:
- Authorea
- Date Published:
- Subject(s) / Keyword(s):
- NUCLEAR FACTOR-Y, Network analysis, AraNet, GeneMANIA, ATTED-II, STRING
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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