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piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks
MOTIVATION: Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is an understudied problem with tremendous potential a...
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| Pubblicato in: | Bioinformatics |
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| Autori principali: | , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Oxford University Press
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6129280/ https://ncbi.nlm.nih.gov/pubmed/30423087 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/bty591 |
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