Identifying large-scale interaction atlases using probabilistic graphs and external knowledge
Abstract Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochasti...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2022-01-01
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| Series: | Journal of Clinical and Translational Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866122000188/type/journal_article |
| _version_ | 1811156496461856768 |
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| author | Sree K. Chanumolu Hasan H. Otu |
| author_facet | Sree K. Chanumolu Hasan H. Otu |
| author_sort | Sree K. Chanumolu |
| collection | DOAJ |
| description |
Abstract
Introduction:
Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability.
Methods:
We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster.
Results:
We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms.
Conclusions:
We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.
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| first_indexed | 2024-04-10T04:52:44Z |
| format | Article |
| id | doaj.art-774198a9cb35465e922fe4cadc040214 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-04-10T04:52:44Z |
| publishDate | 2022-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-774198a9cb35465e922fe4cadc0402142023-03-09T12:31:05ZengCambridge University PressJournal of Clinical and Translational Science2059-86612022-01-01610.1017/cts.2022.18Identifying large-scale interaction atlases using probabilistic graphs and external knowledgeSree K. Chanumolu0Hasan H. Otu1https://orcid.org/0000-0002-9253-8152Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USADepartment of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA Abstract Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability. Methods: We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster. Results: We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms. Conclusions: We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas. https://www.cambridge.org/core/product/identifier/S2059866122000188/type/journal_articleInteractomeatlasgene interaction networkexternal knowledgeBayesian networks |
| spellingShingle | Sree K. Chanumolu Hasan H. Otu Identifying large-scale interaction atlases using probabilistic graphs and external knowledge Journal of Clinical and Translational Science Interactome atlas gene interaction network external knowledge Bayesian networks |
| title | Identifying large-scale interaction atlases using probabilistic graphs and external knowledge |
| title_full | Identifying large-scale interaction atlases using probabilistic graphs and external knowledge |
| title_fullStr | Identifying large-scale interaction atlases using probabilistic graphs and external knowledge |
| title_full_unstemmed | Identifying large-scale interaction atlases using probabilistic graphs and external knowledge |
| title_short | Identifying large-scale interaction atlases using probabilistic graphs and external knowledge |
| title_sort | identifying large scale interaction atlases using probabilistic graphs and external knowledge |
| topic | Interactome atlas gene interaction network external knowledge Bayesian networks |
| url | https://www.cambridge.org/core/product/identifier/S2059866122000188/type/journal_article |
| work_keys_str_mv | AT sreekchanumolu identifyinglargescaleinteractionatlasesusingprobabilisticgraphsandexternalknowledge AT hasanhotu identifyinglargescaleinteractionatlasesusingprobabilisticgraphsandexternalknowledge |