Netrank: network-based approach for biomarker discovery
Abstract Background Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05418-6 |
| _version_ | 1797769303691362304 |
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| author | Ali Al-Fatlawi Eka Rusadze Alexander Shmelkin Negin Malekian Cigdem Ozen Christian Pilarsky Michael Schroeder |
| author_facet | Ali Al-Fatlawi Eka Rusadze Alexander Shmelkin Negin Malekian Cigdem Ozen Christian Pilarsky Michael Schroeder |
| author_sort | Ali Al-Fatlawi |
| collection | DOAJ |
| description | Abstract Background Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA). Results The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank’s biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures. Conclusion In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper. |
| first_indexed | 2024-03-12T21:06:56Z |
| format | Article |
| id | doaj.art-60554c095f214540ae647794148df3bc |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-12T21:06:56Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-60554c095f214540ae647794148df3bc2023-07-30T11:25:56ZengBMCBMC Bioinformatics1471-21052023-07-0124111010.1186/s12859-023-05418-6Netrank: network-based approach for biomarker discoveryAli Al-Fatlawi0Eka Rusadze1Alexander Shmelkin2Negin Malekian3Cigdem Ozen4Christian Pilarsky5Michael Schroeder6Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering, Technische Universität DresdenBiotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering, Technische Universität DresdenBiotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering, Technische Universität DresdenBiotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering, Technische Universität DresdenBiotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering, Technische Universität DresdenDepartment of Surgical Research, Universitätsklinikum ErlangenBiotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering, Technische Universität DresdenAbstract Background Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA). Results The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank’s biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures. Conclusion In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper.https://doi.org/10.1186/s12859-023-05418-6BiomarkerCancerProtein networksRNAGene expressionR package |
| spellingShingle | Ali Al-Fatlawi Eka Rusadze Alexander Shmelkin Negin Malekian Cigdem Ozen Christian Pilarsky Michael Schroeder Netrank: network-based approach for biomarker discovery BMC Bioinformatics Biomarker Cancer Protein networks RNA Gene expression R package |
| title | Netrank: network-based approach for biomarker discovery |
| title_full | Netrank: network-based approach for biomarker discovery |
| title_fullStr | Netrank: network-based approach for biomarker discovery |
| title_full_unstemmed | Netrank: network-based approach for biomarker discovery |
| title_short | Netrank: network-based approach for biomarker discovery |
| title_sort | netrank network based approach for biomarker discovery |
| topic | Biomarker Cancer Protein networks RNA Gene expression R package |
| url | https://doi.org/10.1186/s12859-023-05418-6 |
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