NeKo: a tool for automatic network construction from prior knowledge
Biological networks provide a structured framework for analyzing the dynamic interplay and interactions between molecular entities, facilitating deeper insights into cellular functions and biological processes. Network construction often requires extensive manual curation based on scientific literat...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
September 16, 2025
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| In: |
PLoS Computational Biology
Year: 2025, Volume: 21, Issue: 9, Pages: 1-17 |
| ISSN: | 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1013300 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1371/journal.pcbi.1013300 Verlag, kostenfrei, Volltext: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013300 
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| Author Notes: | Marco Ruscone, Eirini Tsirvouli, Andrea Checcoli, Denes Turei, Emmanuel Barillot, Julio Saez-Rodriguez, Loredana Martignetti, Åsmund Flobak, Laurence Calzone |
MARC
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| 520 | |a Biological networks provide a structured framework for analyzing the dynamic interplay and interactions between molecular entities, facilitating deeper insights into cellular functions and biological processes. Network construction often requires extensive manual curation based on scientific literature and public databases, a time-consuming and laborious task. To address this challenge, we introduce NeKo, a Python package to automate the construction of biological networks by integrating and prioritizing molecular interactions from various databases. NeKo allows users to provide their molecules of interest (e.g., genes, proteins or phosphosites), select interaction resources and apply flexible strategies to build networks based on prior knowledge. Users can filter interactions by various criteria, such as direct or indirect links and signed or unsigned interactions, to tailor the network to their needs and downstream analysis. We demonstrate some of NeKo’s capabilities in two use cases: first we construct a network based on transcriptomics from medulloblastoma; in the second, we model drug synergies. NeKo streamlines the network-building process, making it more accessible and efficient for researchers. | ||
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| 650 | 4 | |a Network analysis | |
| 650 | 4 | |a Protein interaction networks | |
| 650 | 4 | |a Protein-protein interactions | |
| 650 | 4 | |a Regulator genes | |
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| 700 | 1 | |a Checcoli, Andrea |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Barillot, Emmanuel |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Martignetti, Loredana |e VerfasserIn |4 aut | |
| 700 | 1 | |a Flobak, Åsmund |e VerfasserIn |4 aut | |
| 700 | 1 | |a Calzone, Laurence |e VerfasserIn |4 aut | |
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