ArticleScientific reports2020
L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis.Frontiers in pharmacology · 2025Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases.Life (Basel, Switzerland) · 2023Article
- Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks.Entropy (Basel, Switzerland) · 2022Article
- Challenges and Limitations of Biological Network Analysis.Biotech (Basel (Switzerland)) · 2022Article
- Using dual-network-analyser for communities detecting in dual networks.BMC bioinformatics · 2022Article
- Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data.BMC bioinformatics · 2021Article
- A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks.Applied network science · 2021Article
- COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data.Network modeling and analysis in health informatics and bioinformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. Many of these algorithms can deal with networks with a single class of nodes and edges, also referred to as homogeneous networks. Recently, many different approaches tried to integrate into a single model the interplay of different molecules. A possible formalism to model such a scenario comes from node/edge coloured networks (also known as heterogeneous networks) implemented as node/ edge-coloured graphs. Therefore, the need for the introduction of algorithms able to compare heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks, and we formulate it as a network alignment problem. To the best of our knowledge, the local alignment of heterogeneous networks has not been explored in the past. We here propose L-HetNetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and builds a local alignment of them. We also implemented and tested our algorithm. Our results confirm that our method builds high-quality alignments. The following website *contains Supplementary File 1 material and the code.
Identifiers
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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.