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Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information
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| Zeitschriftentitel: | Computational and Mathematical Methods in Medicine |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Computational and Mathematical Methods in Medicine, 2017, 2017, S. 1-10 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Hindawi Limited
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| Schlagwörter: |
| author_facet |
Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon |
|---|---|
| author |
Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon |
| spellingShingle |
Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon Computational and Mathematical Methods in Medicine Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information Applied Mathematics General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology Modeling and Simulation General Medicine |
| author_sort |
kim, sunghwan |
| spelling |
Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon 1748-670X 1748-6718 Hindawi Limited Applied Mathematics General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology Modeling and Simulation General Medicine http://dx.doi.org/10.1155/2017/8520480 <jats:p>Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.</jats:p> Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information Computational and Mathematical Methods in Medicine |
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| title |
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_unstemmed |
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_full |
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_fullStr |
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_full_unstemmed |
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_short |
Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_sort |
node-structured integrative gaussian graphical model guided by pathway information |
| topic |
Applied Mathematics General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology Modeling and Simulation General Medicine |
| url |
http://dx.doi.org/10.1155/2017/8520480 |
| publishDate |
2017 |
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1-10 |
| description |
<jats:p>Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.</jats:p> |
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| author | Kim, SungHwan, Jhong, Jae-Hwan, Lee, JungJun, Koo, Ja-Yong, Lee, ByungYong, Han, SungWon |
| author_facet | Kim, SungHwan, Jhong, Jae-Hwan, Lee, JungJun, Koo, Ja-Yong, Lee, ByungYong, Han, SungWon, Kim, SungHwan, Jhong, Jae-Hwan, Lee, JungJun, Koo, Ja-Yong, Lee, ByungYong, Han, SungWon |
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| description | <jats:p>Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.</jats:p> |
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| spelling | Kim, SungHwan Jhong, Jae-Hwan Lee, JungJun Koo, Ja-Yong Lee, ByungYong Han, SungWon 1748-670X 1748-6718 Hindawi Limited Applied Mathematics General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology Modeling and Simulation General Medicine http://dx.doi.org/10.1155/2017/8520480 <jats:p>Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.</jats:p> Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information Computational and Mathematical Methods in Medicine |
| spellingShingle | Kim, SungHwan, Jhong, Jae-Hwan, Lee, JungJun, Koo, Ja-Yong, Lee, ByungYong, Han, SungWon, Computational and Mathematical Methods in Medicine, Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information, Applied Mathematics, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, Modeling and Simulation, General Medicine |
| title | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_full | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_fullStr | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_full_unstemmed | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_short | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| title_sort | node-structured integrative gaussian graphical model guided by pathway information |
| title_unstemmed | Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |
| topic | Applied Mathematics, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, Modeling and Simulation, General Medicine |
| url | http://dx.doi.org/10.1155/2017/8520480 |