Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways
Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2014-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.4137/CIN.S14054 |
| _version_ | 1819079681195900928 |
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| author | Nancy Lan Guo Ying-Wooi Wan |
| author_facet | Nancy Lan Guo Ying-Wooi Wan |
| author_sort | Nancy Lan Guo |
| collection | DOAJ |
| description | Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database. |
| first_indexed | 2024-12-21T19:32:51Z |
| format | Article |
| id | doaj.art-0e0cef11550548cba978a5133a7fc0ee |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-21T19:32:51Z |
| publishDate | 2014-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-0e0cef11550548cba978a5133a7fc0ee2022-12-21T18:52:40ZengSAGE PublishingCancer Informatics1176-93512014-01-0113s510.4137/CIN.S14054Network-Based Identification of Biomarkers Coexpressed with Multiple PathwaysNancy Lan Guo0Ying-Wooi Wan1Mary Babb Randolph Cancer Center/School of Public Health, West Virginia University, Morgantown, WV, USA.Mary Babb Randolph Cancer Center/School of Public Health, West Virginia University, Morgantown, WV, USA.Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.https://doi.org/10.4137/CIN.S14054 |
| spellingShingle | Nancy Lan Guo Ying-Wooi Wan Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways Cancer Informatics |
| title | Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways |
| title_full | Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways |
| title_fullStr | Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways |
| title_full_unstemmed | Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways |
| title_short | Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways |
| title_sort | network based identification of biomarkers coexpressed with multiple pathways |
| url | https://doi.org/10.4137/CIN.S14054 |
| work_keys_str_mv | AT nancylanguo networkbasedidentificationofbiomarkerscoexpressedwithmultiplepathways AT yingwooiwan networkbasedidentificationofbiomarkerscoexpressedwithmultiplepathways |