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gene_networks_inference [2019/07/23 15:51] – [Related work] admingene_networks_inference [2019/09/04 03:56] – [Related work] admin
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   - The [[https://www.nature.com/articles/s41586-018-0623-z#Sec38|BEAT]] ALM dataset of ~300 cases including gene expression, survival, ELN17, etc.   - The [[https://www.nature.com/articles/s41586-018-0623-z#Sec38|BEAT]] ALM dataset of ~300 cases including gene expression, survival, ELN17, etc.
   - [[https://www.leukemiaatlas.org/adultaml|Leukemia Protein Atlas]]: Expression of hundreds of proteins were measured in bone marrow and PB samples of ~200 AML cases. A good publicly available resource to validate findings based on gene expression assays.   - [[https://www.leukemiaatlas.org/adultaml|Leukemia Protein Atlas]]: Expression of hundreds of proteins were measured in bone marrow and PB samples of ~200 AML cases. A good publicly available resource to validate findings based on gene expression assays.
 +  - ~40K [[https://www.cell.com/cell/fulltext/S0092-8674(19)30094-7?_returnURL=https://linkinghub.elsevier.com/retrieve/pii/S0092867419300947?showall=true|single cell]] RNA-Seq data from 40 bone marrow aspirates, including 16 AML patients and \\  5 healthy donors.
  
  
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   - Guillamot, Maria, Luisa Cimmino, and Iannis Aifantis. "The impact of DNA methylation in hematopoietic malignancies." [[http://www.cell.com/trends/cancer/pdf/S2405-8033(15)00089-8.pdf|Trends in cancer]] 2.2 (2016): 70-83. \\  Reviews and references DNA methylation studies and datasets on AML. E.g., [[http://www.sciencedirect.com/science/article/pii/S1535610809004206|Figueroa]] et al. used DNA methylation for classification of 344 AML cases. [[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002781|Akalin]] et al. related DNA methylation patterns with mutations in 5 AML cases. "The methylation status of specific genes can predict the future survival of AML patients, suggesting that DNA methylation is a biomarker for clinical outcome" see e.g., Figueroa et al, [[http://www.bloodjournal.org/content/bloodjournal/113/6/1315.full.pdf?sso-checked=true|Jiang]] 2009 (studied MDS to AML progression in 184 cases), and [[http://www.bloodjournal.org/content/115/3/636.long?sso-checked=true|Bullinger]] 2010 (analyzed 92 genomic regions in 182 patients).   - Guillamot, Maria, Luisa Cimmino, and Iannis Aifantis. "The impact of DNA methylation in hematopoietic malignancies." [[http://www.cell.com/trends/cancer/pdf/S2405-8033(15)00089-8.pdf|Trends in cancer]] 2.2 (2016): 70-83. \\  Reviews and references DNA methylation studies and datasets on AML. E.g., [[http://www.sciencedirect.com/science/article/pii/S1535610809004206|Figueroa]] et al. used DNA methylation for classification of 344 AML cases. [[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002781|Akalin]] et al. related DNA methylation patterns with mutations in 5 AML cases. "The methylation status of specific genes can predict the future survival of AML patients, suggesting that DNA methylation is a biomarker for clinical outcome" see e.g., Figueroa et al, [[http://www.bloodjournal.org/content/bloodjournal/113/6/1315.full.pdf?sso-checked=true|Jiang]] 2009 (studied MDS to AML progression in 184 cases), and [[http://www.bloodjournal.org/content/115/3/636.long?sso-checked=true|Bullinger]] 2010 (analyzed 92 genomic regions in 182 patients).
   - John [[https://www.youtube.com/watch?v=Vyhq7GZFnes|Quackenbush's]] talk entitled: "Using Networks to Understand the Genotype-Phenotype Connection".   - John [[https://www.youtube.com/watch?v=Vyhq7GZFnes|Quackenbush's]] talk entitled: "Using Networks to Understand the Genotype-Phenotype Connection".
-  - Saelens, Wouter, Robrecht Cannoodt, and Yvan Saeys. "A comprehensive evaluation of module detection methods for gene expression data." [[https://www.nature.com/articles/s41467-018-03424-4|Nature communications]] 9.1 (2018): 1090.  \\  "Graph-based, representative-based, and hierarchical clustering all performed equally well, with the clustering method FLAME (Fuzzy clustering by Local Approximation of Memberships), one of the only clustering methods able to detect overlap, slightly outperforming other clustering methods" including WGCNA. Regularity networks that had been inferred using other data, e.g., "binding motifs in active enhancers", were used as gold standard.+  - Saelens, Wouter, Robrecht Cannoodt, and Yvan Saeys. "A comprehensive evaluation of module detection methods for gene expression data." [[https://www.nature.com/articles/s41467-018-03424-4|Nature communications]] 9.1 (2018): 1090. \\  "Graph-based, representative-based, and hierarchical clustering all performed equally well, with the clustering method FLAME (Fuzzy clustering by Local Approximation of Memberships), one of the only clustering methods able to detect overlap, slightly outperforming other clustering methods" including WGCNA. Regularity networks that had been inferred using other data, e.g., "binding motifs in active enhancers", were used as gold standard. 
 +  - Choobdar, Sarvenaz, et al. "Assessment of network module identification across complex diseases." [[https://www.nature.com/articles/s41592-019-0509-5|Nature Methods]] 16.9 (2019): 843-852. \\  "The popular weighted gene co-expression network analysis (WGCNA) method7 did not perform competitively."