ArticleScientific reports2018
From homogeneous to heterogeneous network alignment via colored graphlets.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Supervised biological network alignment with graph neural networks.Bioinformatics (Oxford, England) · 2023Article
- Biology and medicine in the landscape of quantum advantages.Journal of the Royal Society, Interface · 2022Review
- A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures.JACS Au · 2022Article
- Using dual-network-analyser for communities detecting in dual networks.BMC bioinformatics · 2022Article
- Encoding edge type information in graphlets.PloS one · 2022Article
- CREB1ACS omega · 2021Article
- Data-driven biological network alignment that uses topological, sequence, and functional information.BMC bioinformatics · 2021Article
- Network Approaches for Dissecting the Immune System.iScience · 2020Review
- Network-based protein structural classification.Royal Society open science · 2020Article
- L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks.Scientific reports · 2020Article
- Pairwise Versus Multiple Global Network Alignment.IEEE access : practical innovations, open solutions · 2020Article
- Data-driven network alignment.PloS one · 2020Article
- A Guide to Conquer the Biological Network Era Using Graph Theory.Frontiers in bioengineering and biotechnology · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network alignment (NA) compares networks with the goal of finding a node mapping that uncovers highly similar (conserved) network regions. Existing NA methods are homogeneous, i.e., they can deal only with networks containing nodes and edges of one type. Due to increasing amounts of heterogeneous network data with nodes or edges of different types, we extend three recent state-of-the-art homogeneous NA methods, WAVE, MAGNA++, and SANA, to allow for heterogeneous NA for the first time. We introduce several algorithmic novelties. Namely, these existing methods compute homogeneous graphlet-based node similarities and then find high-scoring alignments with respect to these similarities, while simultaneously maximizing the amount of conserved edges. Instead, we extend homogeneous graphlets to their heterogeneous counterparts, which we then use to develop a new measure of heterogeneous node similarity. Also, we extend S
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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.