Evaluation of network-guided random forest for disease gene discovery
Abstract Background Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where th...
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-024-00361-5 |
| _version_ | 1797199597224853504 |
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| author | Jianchang Hu Silke Szymczak |
| author_facet | Jianchang Hu Silke Szymczak |
| author_sort | Jianchang Hu |
| collection | DOAJ |
| description | Abstract Background Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. Results Our simulation results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes. Conclusions Gene networks can provide additional information to aid the gene expression analysis for disease module and pathway identification. But they need to be used with caution and validation on the results need to be carried out to guard against spurious gene selection. More robust approaches to incorporate such information into RF construction also warrant further study. |
| first_indexed | 2024-04-24T07:18:17Z |
| format | Article |
| id | doaj.art-3ae11e314e034c4091fa5e0c22dffb58 |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-04-24T07:18:17Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-3ae11e314e034c4091fa5e0c22dffb582024-04-21T11:10:55ZengBMCBioData Mining1756-03812024-04-0117111910.1186/s13040-024-00361-5Evaluation of network-guided random forest for disease gene discoveryJianchang Hu0Silke Szymczak1Institute of Medical Biometry and Statistics, University of LübeckInstitute of Medical Biometry and Statistics, University of LübeckAbstract Background Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. Results Our simulation results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes. Conclusions Gene networks can provide additional information to aid the gene expression analysis for disease module and pathway identification. But they need to be used with caution and validation on the results need to be carried out to guard against spurious gene selection. More robust approaches to incorporate such information into RF construction also warrant further study.https://doi.org/10.1186/s13040-024-00361-5Gene expressionProtein-protein interactionRNA-SeqWeighted random forest |
| spellingShingle | Jianchang Hu Silke Szymczak Evaluation of network-guided random forest for disease gene discovery BioData Mining Gene expression Protein-protein interaction RNA-Seq Weighted random forest |
| title | Evaluation of network-guided random forest for disease gene discovery |
| title_full | Evaluation of network-guided random forest for disease gene discovery |
| title_fullStr | Evaluation of network-guided random forest for disease gene discovery |
| title_full_unstemmed | Evaluation of network-guided random forest for disease gene discovery |
| title_short | Evaluation of network-guided random forest for disease gene discovery |
| title_sort | evaluation of network guided random forest for disease gene discovery |
| topic | Gene expression Protein-protein interaction RNA-Seq Weighted random forest |
| url | https://doi.org/10.1186/s13040-024-00361-5 |
| work_keys_str_mv | AT jianchanghu evaluationofnetworkguidedrandomforestfordiseasegenediscovery AT silkeszymczak evaluationofnetworkguidedrandomforestfordiseasegenediscovery |