Graphical Abstract.
<p>The first box shows how the COPD and the Differential partial correlation networks have been built. Given a source of biological information, such as a PPI network or set of functional annotations, for every gene i and j (g<sub>i</sub>,g<sub>j</sub>) we extract the g...
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| مؤلفون آخرون: | , , , , , , , , , , , |
| منشور في: |
2024
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| _version_ | 1852025871933636608 |
|---|---|
| author | Michele Gentili (19865208) |
| author2 | Kimberly Glass (416881) Enrico Maiorino (6956657) Brian D. Hobbs (5118383) Zhonghui Xu (659900) Peter J. Castaldi (8788598) Michael H. Cho (6856079) Craig P. Hersh (8543712) Dandi Qiao (5118380) Jarrett D. Morrow (3082395) Vincent J. Carey (19865211) John Platig (3115692) Edwin K. Silverman (6856082) |
| author2_role | author author author author author author author author author author author author |
| author_facet | Michele Gentili (19865208) Kimberly Glass (416881) Enrico Maiorino (6956657) Brian D. Hobbs (5118383) Zhonghui Xu (659900) Peter J. Castaldi (8788598) Michael H. Cho (6856079) Craig P. Hersh (8543712) Dandi Qiao (5118380) Jarrett D. Morrow (3082395) Vincent J. Carey (19865211) John Platig (3115692) Edwin K. Silverman (6856082) |
| author_role | author |
| dc.creator.none.fl_str_mv | Michele Gentili (19865208) Kimberly Glass (416881) Enrico Maiorino (6956657) Brian D. Hobbs (5118383) Zhonghui Xu (659900) Peter J. Castaldi (8788598) Michael H. Cho (6856079) Craig P. Hersh (8543712) Dandi Qiao (5118380) Jarrett D. Morrow (3082395) Vincent J. Carey (19865211) John Platig (3115692) Edwin K. Silverman (6856082) |
| dc.date.none.fl_str_mv | 2024-10-17T17:37:51Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1011079.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Graphical_Abstract_/27251460 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Microbiology Cell Biology Genetics Molecular Biology Science Policy Biological Sciences not elsewhere classified established environmental exposures complex disease influenced (~ 70mb ), 4q region bounded identified several co copd genetic susceptibility partial correlations informed including signals near div >< p genes differentially co copd vs controls partial correlations region enriched genomic region differential co copd genome copd cases upon clustering tet2 </ tet2 )</ spp1 </ seq data regional cluster previously implicated ppm1k </ ppa2 </ lung tissue leveraging rna hhip </ gstcd </ fam13a </ cigarette smoking btc </ based knock |
| dc.title.none.fl_str_mv | Graphical Abstract. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The first box shows how the COPD and the Differential partial correlation networks have been built. Given a source of biological information, such as a PPI network or set of functional annotations, for every gene i and j (g<sub>i</sub>,g<sub>j</sub>) we extract the gene specific regularization vector <i>λ</i><sub><i>i</i>,<i>j</i></sub>, that contains the distance between gene g<sub>i</sub> and g<sub>j</sub> to all of the other genes, in our case the inverse of the personalized PageRank algorithm (more details in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1011079#pcbi.1011079.s001" target="_blank">S1 Text</a></b>). For every pair of genes (g<sub>i</sub>,g<sub>j</sub>), we compute the Gene-Specific Ridge Partial Correlation. Finally, to build the partial correlation network we run a t-test, using permutation and bootstrap procedures, respectively. The second box contains the analysis done on the two networks from (1) the COPD subjects and (2) a differential network of COPD patients vs Healthy controls. Both analyses provide insights into the presence of a potential co-regulatory network in this genomic region.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_aafff72fcd8aecde1a03a5c5b080a7ac |
| identifier_str_mv | 10.1371/journal.pcbi.1011079.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27251460 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Graphical Abstract.Michele Gentili (19865208)Kimberly Glass (416881)Enrico Maiorino (6956657)Brian D. Hobbs (5118383)Zhonghui Xu (659900)Peter J. Castaldi (8788598)Michael H. Cho (6856079)Craig P. Hersh (8543712)Dandi Qiao (5118380)Jarrett D. Morrow (3082395)Vincent J. Carey (19865211)John Platig (3115692)Edwin K. Silverman (6856082)MicrobiologyCell BiologyGeneticsMolecular BiologyScience PolicyBiological Sciences not elsewhere classifiedestablished environmental exposurescomplex disease influenced(~ 70mb ),4q region boundedidentified several cocopd genetic susceptibilitypartial correlations informedincluding signals neardiv >< pgenes differentially cocopd vs controlspartial correlationsregion enrichedgenomic regiondifferential cocopd genomecopd casesupon clusteringtet2 </tet2 )</spp1 </seq dataregional clusterpreviously implicatedppm1k </ppa2 </lung tissueleveraging rnahhip </gstcd </fam13a </cigarette smokingbtc </based knock<p>The first box shows how the COPD and the Differential partial correlation networks have been built. Given a source of biological information, such as a PPI network or set of functional annotations, for every gene i and j (g<sub>i</sub>,g<sub>j</sub>) we extract the gene specific regularization vector <i>λ</i><sub><i>i</i>,<i>j</i></sub>, that contains the distance between gene g<sub>i</sub> and g<sub>j</sub> to all of the other genes, in our case the inverse of the personalized PageRank algorithm (more details in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1011079#pcbi.1011079.s001" target="_blank">S1 Text</a></b>). For every pair of genes (g<sub>i</sub>,g<sub>j</sub>), we compute the Gene-Specific Ridge Partial Correlation. Finally, to build the partial correlation network we run a t-test, using permutation and bootstrap procedures, respectively. The second box contains the analysis done on the two networks from (1) the COPD subjects and (2) a differential network of COPD patients vs Healthy controls. Both analyses provide insights into the presence of a potential co-regulatory network in this genomic region.</p>2024-10-17T17:37:51ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1011079.g002https://figshare.com/articles/figure/Graphical_Abstract_/27251460CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272514602024-10-17T17:37:51Z |
| spellingShingle | Graphical Abstract. Michele Gentili (19865208) Microbiology Cell Biology Genetics Molecular Biology Science Policy Biological Sciences not elsewhere classified established environmental exposures complex disease influenced (~ 70mb ), 4q region bounded identified several co copd genetic susceptibility partial correlations informed including signals near div >< p genes differentially co copd vs controls partial correlations region enriched genomic region differential co copd genome copd cases upon clustering tet2 </ tet2 )</ spp1 </ seq data regional cluster previously implicated ppm1k </ ppa2 </ lung tissue leveraging rna hhip </ gstcd </ fam13a </ cigarette smoking btc </ based knock |
| status_str | publishedVersion |
| title | Graphical Abstract. |
| title_full | Graphical Abstract. |
| title_fullStr | Graphical Abstract. |
| title_full_unstemmed | Graphical Abstract. |
| title_short | Graphical Abstract. |
| title_sort | Graphical Abstract. |
| topic | Microbiology Cell Biology Genetics Molecular Biology Science Policy Biological Sciences not elsewhere classified established environmental exposures complex disease influenced (~ 70mb ), 4q region bounded identified several co copd genetic susceptibility partial correlations informed including signals near div >< p genes differentially co copd vs controls partial correlations region enriched genomic region differential co copd genome copd cases upon clustering tet2 </ tet2 )</ spp1 </ seq data regional cluster previously implicated ppm1k </ ppa2 </ lung tissue leveraging rna hhip </ gstcd </ fam13a </ cigarette smoking btc </ based knock |