ArticleFrontiers in bioengineering and biotechnology2020
NAIGO: An Improved Method to Align PPI Networks Based on Gene Ontology and Graphlets.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 17 citations in OpenAlex.
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- Towards a hierarchical approach and scalable semi-local centrality for key node identification in weighted complex networks.Scientific reports · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- Taiwan's Smart Healthcare Value Chain: AI Innovation from R&D to Industry Deployment.Healthcare (Basel, Switzerland) · 2025Review
- Botulinum Toxin Treatment in Hereditary Spastic Paraplegia-A Comprehensive Review and Update.Toxins · 2025Review
- Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis.Frontiers in oncology · 2023Article
- Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence.Computational intelligence and neuroscience · 2022Article
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of high throughput technologies, there are more and more protein-protein interaction (PPI) networks available, which provide a need for efficient computational tools for network alignment. Network alignment is widely used to predict functions of certain proteins, identify conserved network modules, and study the evolutionary relationship across species or biological entities. However, network alignment is an NP-complete problem, and previous algorithms are usually slow or less accurate in aligning big networks like human vs. yeast. In this study, we proposed a fast yet accurate algorithm called Network Alignment by Integrating Biological Process (NAIGO). Specifically, we first divided the networks into subnets taking the advantage of known prior knowledge, such as gene ontology. For each subnet pair, we then developed a novel method to align them by considering both protein orthologous information and their local structural information. After that, we expanded the obtained local network alignments in a greedy manner. Taking the aligned pairs as seeds, we formulated the global network alignment problem as an assignment problem based on similarity matrix, which was solved by the Hungarian method. We applied NAIGO to align human and
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