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| - Zhang, B. //et al.// Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. //[[http://www.cell.com/abstract/S0092-8674(13)00387-5|Cell]]//**153**, 707–720 (2013). [{{:zhang2013.pdf|pdf}} ] \\ Methodology is based on [2] plus they train **Bayesian networks** to infer causal structure based on RNA-seq. MCMC was used to optimize BIC score, and \\ 1/3 of 1000 network models were averaged.[[http://www.alzforum.org/webinars/can-network-analysis-identify-pathological-pathways-alzheimers|This]] webinar is a high level description of the methodology. | - Zhang, B. //et al.// Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. //[[http://www.cell.com/abstract/S0092-8674(13)00387-5|Cell]]//**153**, 707–720 (2013). [{{:zhang2013.pdf|pdf}} ] \\ Methodology is based on [2] plus they train **Bayesian networks** to infer causal structure based on RNA-seq. MCMC was used to optimize BIC score, and \\ 1/3 of 1000 network models were averaged.[[http://www.alzforum.org/webinars/can-network-analysis-identify-pathological-pathways-alzheimers|This]] webinar is a high level description of the methodology. |
| - Emilsson, V. //et al.// Genetics of gene expression and its effect on disease.//[[http://www.nature.com/nature/journal/v452/n7186/full/nature06758.html|Nature]]//**452**, 423–428 (2008). [[:http:file_view_emilsson2008.pdf_521380262_emilsson2008.pdf|pdf]], {{:emilsson2008-supp.pdf|Supp}} ] (based on their references [29-31], uses [[http://www.sciencemag.org/content/297/5586/1551.full.pdf|this]] topological overlap measure) | - Emilsson, V. //et al.// Genetics of gene expression and its effect on disease.//[[http://www.nature.com/nature/journal/v452/n7186/full/nature06758.html|Nature]]//**452**, 423–428 (2008). [[:http:file_view_emilsson2008.pdf_521380262_emilsson2008.pdf|pdf]], {{:emilsson2008-supp.pdf|Supp}} ] (based on their references [29-31], uses [[http://www.sciencemag.org/content/297/5586/1551.full.pdf|this]] topological overlap measure) |
| - Identifying Gene Regulatory Networks from Gene Expression Data, [[http://web.cs.ucdavis.edu/~filkov/papers/chapter.pdf|Handbook]] of Computational Molecular Biology (2005) [{{:filkov_chapter27.pdf|pdf}} ]. \\ A good but old book chapter. | - Identifying Gene Regulatory Networks from Gene Expression Data, [[http://web.cs.ucdavis.edu/~filkov/papers/chapter.pdf|Handbook]] of Computational Molecular Biology (2005) [{{:filkov_chapter27.pdf|pdf}} ]. \\ A good but old book chapter. |
| - [[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020130|Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight]] (Identified modules in networks, "A pair of genes is said to have high topological overlap if they are both strongly connected to the same group of genes.", [[http://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/MouseWeight/|Software]]) | - [[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020130|Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight]] (Identified modules in networks, "A pair of genes is said to have high topological overlap if they are both strongly connected to the same group of genes.", [[http://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/MouseWeight/|Software]]) |
| - Gene correlation network analysis [Wikipedia [[http://en.wikipedia.org/wiki/Weighted_correlation_network_analysis|page]]] | - Gene correlation network analysis [Wikipedia [[http://en.wikipedia.org/wiki/Weighted_correlation_network_analysis|page]]] |
| - Friedman, Nir, et al. "Using Bayesian networks to analyze expression data." Journal of computational biology 7.3-4 (2000): 601-620.[{{:friedman2000.pdf|pdf}} ] \\ A relatively old but highly cited, (~2700) original paper. | - Friedman, Nir, et al. "Using Bayesian networks to analyze expression data." Journal of computational biology 7.3-4 (2000): 601-620.[{{:friedman2000.pdf|pdf}} ] \\ A relatively old but highly cited, (~2700) original paper. |
| - Ruan, Jianhua, Angela K. Dean, and Weixiong Zhang. "A general co-expression network-based approach to gene expression analysis: comparison and applications." //[[http://www.biomedcentral.com/1752-0509/4/8|BMC]] systems biology//4.1 (2010): 8. (Dr. Jianhua Ruan from San Antonino) | - Ruan, Jianhua, Angela K. Dean, and Weixiong Zhang. "A general co-expression network-based approach to gene expression analysis: comparison and applications." //[[http://www.biomedcentral.com/1752-0509/4/8|BMC]] systems biology//4.1 (2010): 8. (Dr. Jianhua Ruan from San Antonino) |
| - Nagrecha, Saurabh, Pawan J. Lingras, and Nitesh V. Chawla. "Comparison of gene co-expression networks and bayesian networks." //Intelligent Information and Database [[https://www3.nd.edu/~dial/papers/ACIIDS2013.pdf|Systems]]// . Springer Berlin Heidelberg, 2013. 507-516. \\ Simple description and some relatively old literature review. \\ "Bayesian networks emerge as a more informative tool to determine the causal structure." | - Nagrecha, Saurabh, Pawan J. Lingras, and Nitesh V. Chawla. "Comparison of gene co-expression networks and bayesian networks." //Intelligent Information and Database [[https://www3.nd.edu/~dial/papers/ACIIDS2013.pdf|Systems]]// . Springer Berlin Heidelberg, 2013. 507-516. \\ Simple description and some relatively old literature review. \\ "Bayesian networks emerge as a more informative tool to determine the causal structure." |
| - Systems Biology: The inference of networks from high dimensional genomics data, lecture by Yeung 2011 [{{:yeun2011-systemsbiology.ppt|ppt}} ] (a good introduction to application of Bayesian networks in co-expression network) | - Systems Biology: The inference of networks from high dimensional genomics data, lecture by Yeung 2011 [{{:yeun2011-systemsbiology.ppt|ppt}} ] (a good introduction to application of Bayesian networks in co-expression network) |
| - Hong, Shengjun, et al. "Canonical correlation analysis for RNA-seq co-expression networks." //Nucleic acids [[http://www.ncbi.nlm.nih.gov/pubmed/23460206|research]]//41.8 (2013): e95-e95. (improved co-expression analysis for RNA-seq) | - Hong, Shengjun, et al. "Canonical correlation analysis for RNA-seq co-expression networks." //Nucleic acids [[http://www.ncbi.nlm.nih.gov/pubmed/23460206|research]]//41.8 (2013): e95-e95. (improved co-expression analysis for RNA-seq) |
| - Li, Bingshan, et al. "[[http://www.nature.com/jid/journal/v134/n7/full/jid201428a.html|Transcriptome Analysis of Psoriasis in a Large Case–Control Sample: RNA-Seq Provides Insights into Disease Mechanisms.]]" //Journal of Investigative Dermatology// (2014). (used co-expression network analysis on RNA-seq from 42 samples to study gene regulatory circuits in psoriasis) | - Li, Bingshan, et al. "[[http://www.nature.com/jid/journal/v134/n7/full/jid201428a.html|Transcriptome Analysis of Psoriasis in a Large Case–Control Sample: RNA-Seq Provides Insights into Disease Mechanisms.]]" //Journal of Investigative Dermatology// (2014). (used co-expression network analysis on RNA-seq from 42 samples to study gene regulatory circuits in psoriasis) |
| - [[http://web.stanford.edu/group/wonglab/doc/RNA-seq-talk-JSM2010.pdf|Analysis]] of RNA‐Seq Data, an introduction by Wong, 2010. | - [[http://web.stanford.edu/group/wonglab/doc/RNA-seq-talk-JSM2010.pdf|Analysis]] of RNA‐Seq Data, an introduction by Wong, 2010. |
| - Ellis, Byron, and Wing Hung Wong. "Learning **causal** Bayesian network structures from experimental [[http://web.stanford.edu/group/wonglab/doc/EllisWong-061025.pdf|data]]." //Journal of the American Statistical Association// 103.482 (2008): 778-789. | - Ellis, Byron, and Wing Hung Wong. "Learning **causal** Bayesian network structures from experimental [[http://web.stanford.edu/group/wonglab/doc/EllisWong-061025.pdf|data]]." //Journal of the American Statistical Association// 103.482 (2008): 778-789. |
| - Barretina et al. "The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity." [[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html|Nature]] 483.7391 (2012): 603-607 [{{:barretina2012.pdf|pdf}} ]. \\ It provides CCLE, a valuable dataset produced by **Novartis**; mRNA expression data, responses of 24 compounds, and targeted sequencing on ~500 cell lines. | - Barretina et al. "The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity." [[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html|Nature]] 483.7391 (2012): 603-607 [{{:barretina2012.pdf|pdf}} ]. \\ It provides CCLE, a valuable dataset produced by **Novartis**; mRNA expression data, responses of 24 compounds, and targeted sequencing on ~500 cell lines. |
| - Friedman, Nir, et al. "Using Bayesian networks to analyze expression data."//Journal of computational [[http://www.cs.huji.ac.il/~nir/Papers/FLNP1Full.pdf|biology]]//7.3-4 (2000): 601-620. \\ Has a good introduction to Bayesian networks and learning causal patterns for beginners. | - Friedman, Nir, et al. "Using Bayesian networks to analyze expression data."//Journal of computational [[http://www.cs.huji.ac.il/~nir/Papers/FLNP1Full.pdf|biology]]//7.3-4 (2000): 601-620. \\ Has a good introduction to Bayesian networks and learning causal patterns for beginners. |
| - Al-Lazikani, Bissan, Udai Banerji, and Paul Workman. "Combinatorial drug therapy for cancer in the post-genomic era." //[[http://www.nature.com/nbt/journal/v30/n7/full/nbt.2284.html|Nature]] biotechnology//30.7 (2012): 679-692. \\ "Combinatorial targeted therapy", a good **survey** including computational methods, successful stories like "identification of synergies between MET and EGFR inhibitors…" | - Al-Lazikani, Bissan, Udai Banerji, and Paul Workman. "Combinatorial drug therapy for cancer in the post-genomic era." //[[http://www.nature.com/nbt/journal/v30/n7/full/nbt.2284.html|Nature]] biotechnology//30.7 (2012): 679-692. \\ "Combinatorial targeted therapy", a good **survey** including computational methods, successful stories like "identification of synergies between MET and EGFR inhibitors…" |
| - Bansal, Mukesh, et al. "How to infer gene networks from expression profiles."//Molecular systems biology// 3.1 (2007). \\ A relatively old survey which supports Banjo. | - Bansal, Mukesh, et al. "How to infer gene networks from expression profiles."//Molecular systems biology// 3.1 (2007). \\ A relatively old survey which supports Banjo. |
| - Mourad, Raphaël, and Christine Sinoquet, eds. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. [[http://books.google.com/books?hl=en&lr=&id=2URhBAAAQBAJ&oi=fnd&pg=PP1&dq=Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics&ots=8Yweyc5bKz&sig=xhaDE9f-JXHNckj-lgwvSrgSl3g#v=onepage&q=Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics&f=false|Oxford]] University Press, 2014. \\ An excellent recent, relevant book ( e.g. pages 24,123,154,223).P155: For gene networks, maximum number of parents is commonly set to 3. | - Mourad, Raphaël, and Christine Sinoquet, eds. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. [[http://books.google.com/books?hl=en&lr=&id=2URhBAAAQBAJ&oi=fnd&pg=PP1&dq=Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics&ots=8Yweyc5bKz&sig=xhaDE9f-JXHNckj-lgwvSrgSl3g#v=onepage&q=Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics&f=false|Oxford]] University Press, 2014. \\ An excellent recent, relevant book ( e.g. pages 24,123,154,223).P155: For gene networks, maximum number of parents is commonly set to 3. |
| - de la Fuente, Alberto. "Gene Network Inference.", [[http://www.springer.com/new & forthcoming titles (default)/book/978-3-642-45160-7|Springer]], 2013 [{{:fuente2013.pdf|pdf}} ]. \\ Figure 1 explains why a network-based approach can be superior to black box machine learning techniques. Literature review on advantages of system approaches over studying single genes. Limited the maximum number of parents per gene to 5. [[http://www.mountsinai.org/profiles/jun-zhu|Zhu]] et al. explains [[http://icahn.mssm.edu/departments-and-institutes/genomics/about/software/rimbanet|RIMBANet]] developed by himself. "Bayesian networks offer the best performance." | - de la Fuente, Alberto. "Gene Network Inference.", [[http://www.springer.com/new & forthcoming titles (default)/book/978-3-642-45160-7|Springer]], 2013 [{{:fuente2013.pdf|pdf}} ]. \\ Figure 1 explains why a network-based approach can be superior to black box machine learning techniques. Literature review on advantages of system approaches over studying single genes. Limited the maximum number of parents per gene to 5. [[http://www.mountsinai.org/profiles/jun-zhu|Zhu]] et al. explains [[http://icahn.mssm.edu/departments-and-institutes/genomics/about/software/rimbanet|RIMBANet]] developed by himself. "Bayesian networks offer the best performance." |
| - Bayesian Networks with R (bnlearn) and Hadoop, 2014, a good [[http://www.slideshare.net/ofermend/bayesian-networks-with-r-and-hadoop|talk]] by \\ Ofer Mendelevitch with introduction to BNs. Discusses large networks too. | - Bayesian Networks with R (bnlearn) and Hadoop, 2014, a good [[http://www.slideshare.net/ofermend/bayesian-networks-with-r-and-hadoop|talk]] by \\ Ofer Mendelevitch with introduction to BNs. Discusses large networks too. |
| - Network Analysis Workshop, Systems Biology Analysis Methods for Genomic, 2013, [[http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/WORKSHOP/2013/|UCLA]]. Talk in Bayesian networks by Jun Zhu. | - Network Analysis Workshop, Systems Biology Analysis Methods for Genomic, 2013, [[http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/WORKSHOP/2013/|UCLA]]. Talk in Bayesian networks by Jun Zhu. |
| - Vignes, Matthieu, et al. "Gene regulatory network reconstruction using bayesian networks, the dantzig selector, the lasso and their meta-analysis." PloS [[http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029165#pone-0029165-g008|one]] 6.12 (2011): e29165. \\ A meta-analysis to combine these inference methods by computing a consensus ranking scheme, ranked 1st among 16 in a DREAM challenge, but its superiority was not confirmed in [[http://link.springer.com/chapter/10.1007/978-3-642-45161-4_2|Allouche]] 2014. Used greedy hill-climbing of Banjo. | - Vignes, Matthieu, et al. "Gene regulatory network reconstruction using bayesian networks, the dantzig selector, the lasso and their meta-analysis." PloS [[http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029165#pone-0029165-g008|one]] 6.12 (2011): e29165. \\ A meta-analysis to combine these inference methods by computing a consensus ranking scheme, ranked 1st among 16 in a DREAM challenge, but its superiority was not confirmed in [[http://link.springer.com/chapter/10.1007/978-3-642-45161-4_2|Allouche]] 2014. Used greedy hill-climbing of Banjo. |
| - Nagarajan, Radhakrishnan, Marco Scutari, and Sophie Lèbre. Bayesian Networks in R. [[http://link.springer.com/book/10.1007/978-1-4614-6446-4|Springer]], 2013 [{{:bn-r.pdf|pdf}} ]. \\ A starter-to-advanced book by the author of bnlearn R package, defines a way of comparing networks in foreword section. | - Nagarajan, Radhakrishnan, Marco Scutari, and Sophie Lèbre. Bayesian Networks in R. [[http://link.springer.com/book/10.1007/978-1-4614-6446-4|Springer]], 2013 [{{:bn-r.pdf|pdf}} ]. \\ A starter-to-advanced book by the author of bnlearn R package, defines a way of comparing networks in foreword section. |
| - Schadt, Eric E., et al. "An integrative genomics approach to infer causal associations between gene expression and disease." [[http://www.nature.com/ng/journal/v37/n7/full/ng1589.html|Nature]] genetics 37.7 (2005): 710-717. \\ The original Schadt paper introducing the idea of using genomic data to infer causality in BNs of expression (LCMS). | - Schadt, Eric E., et al. "An integrative genomics approach to infer causal associations between gene expression and disease." [[http://www.nature.com/ng/journal/v37/n7/full/ng1589.html|Nature]] genetics 37.7 (2005): 710-717. \\ The original Schadt paper introducing the idea of using genomic data to infer causality in BNs of expression (LCMS). |
| - Jiang, Xia, et al. "Learning genetic epistasis using Bayesian network scoring criteria." BMC [[http://www.biomedcentral.com/1471-2105/12/89/|bioinformatics]] 12.1 (2011): 89. \\ On simulated data, Bayesian scoring (BDeu) outperforms minimum description scores such as AIC. | - Jiang, Xia, et al. "Learning genetic epistasis using Bayesian network scoring criteria." BMC [[http://www.biomedcentral.com/1471-2105/12/89/|bioinformatics]] 12.1 (2011): 89. \\ On simulated data, Bayesian scoring (BDeu) outperforms minimum description scores such as AIC. |
| - Yu, Jing, et al. "Using Bayesian network inference algorithms to recover molecular genetic regulatory networks." //3rd International [[http://ftp.cs.duke.edu/~amink/publications/manuscripts/hartemink02.icsb.pdf|Conference]] on Systems Biology//. 2002. \\ From Hartemink group, DBN. Very good explanation and comparison of different scores and search strategies. "With large amounts of data, the BIC is a good approximation to the full posterior (BDe) score and is faster to compute; however, it is known to over-penalize with small amounts of data." "The BDe score works better than the BIC score in recovering genetic regulatory pathways." [Compared to simulated annealing and genetic algorithm,] greedy search is better as it can find the top graph in the least amount of time" (note that their network is small). " 3-category discretization was optimal. | - Yu, Jing, et al. "Using Bayesian network inference algorithms to recover molecular genetic regulatory networks." //3rd International [[http://ftp.cs.duke.edu/~amink/publications/manuscripts/hartemink02.icsb.pdf|Conference]] on Systems Biology//. 2002. \\ From Hartemink group, DBN. Very good explanation and comparison of different scores and search strategies. "With large amounts of data, the BIC is a good approximation to the full posterior (BDe) score and is faster to compute; however, it is known to over-penalize with small amounts of data." "The BDe score works better than the BIC score in recovering genetic regulatory pathways." [Compared to simulated annealing and genetic algorithm,] greedy search is better as it can find the top graph in the least amount of time" (note that their network is small). " 3-category discretization was optimal. |
| - Zhu, Jun, et al. "Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations." [[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030069|PLoS]] computational biology 3.4 (2007): e69. \\ The methodology used to learn BNs in Zhang et al. 2013 AD paper. | - Zhu, Jun, et al. "Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations." [[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030069|PLoS]] computational biology 3.4 (2007): e69. \\ The methodology used to learn BNs in Zhang et al. 2013 AD paper. |
| - Zhu, J., et al. "An integrative genomics approach to the reconstruction of gene networks in segregating populations." [[http://www.ncbi.nlm.nih.gov/pubmed/15237224|Cytogenetic]] and genome research 105.2-4 (2004): 363-374 [{{:zhu2004.pdf|pdf}} ]. \\ The methodology used to incorporate genetic data to better learn BNs (e.g. useful in Zhang et al. 2013 AD paper). Edges which appeared in more than 30% of 1000 graphs should be used to make the consensus graph. | - Zhu, J., et al. "An integrative genomics approach to the reconstruction of gene networks in segregating populations." [[http://www.ncbi.nlm.nih.gov/pubmed/15237224|Cytogenetic]] and genome research 105.2-4 (2004): 363-374 [{{:zhu2004.pdf|pdf}} ]. \\ The methodology used to incorporate genetic data to better learn BNs (e.g. useful in Zhang et al. 2013 AD paper). Edges which appeared in more than 30% of 1000 graphs should be used to make the consensus graph. |
| - Steidl, Ulrich G., and Constantine S. Mitsiades. "Therapeutic and diagnostic target gene in acute myeloid leukemia." U.S. [[http://www.google.com/patents/US20140187604|Patent]] Application 14/113,405. \\ Useful for sanity check to see if the genes we identify are known to be important. | - Steidl, Ulrich G., and Constantine S. Mitsiades. "Therapeutic and diagnostic target gene in acute myeloid leukemia." U.S. [[http://www.google.com/patents/US20140187604|Patent]] Application 14/113,405. \\ Useful for sanity check to see if the genes we identify are known to be important. |
| - Kommadath, Arun, et al. "Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding." //BMC [[http://www.biomedcentral.com/1471-2164/15/452|genomics]]//15.1 (2014): 452. \\ A recent study that used WGCNA on RNA-seq. | - Kommadath, Arun, et al. "Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding." //BMC [[http://www.biomedcentral.com/1471-2164/15/452|genomics]]//15.1 (2014): 452. \\ A recent study that used WGCNA on RNA-seq. |
| - Gillis, Jesse, and Paul Pavlidis. "“Guilt by association” is the exception rather than the rule in gene networks." [[http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002444|PLoS]] computational biology 8.3 (2012): e1002444. \\ Discussed the difficulties of computational network analysis. "…Functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network". | - Gillis, Jesse, and Paul Pavlidis. "“Guilt by association” is the exception rather than the rule in gene networks." [[http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002444|PLoS]] computational biology 8.3 (2012): e1002444. \\ Discussed the difficulties of computational network analysis. "…Functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network". |
| - Koski, Timo JT, and John Noble. "A review of bayesian networks and structure learning." [[http://www-users.mat.umk.pl/~wniem/SemMgr/BayesNets/Bayesnetsreview.pdf|Mathematica]] Applicanda 40.1 (2012): 51-103. \\ A review from mathematical view point including applications of algebraic geometry to Bayesian networks! | - Koski, Timo JT, and John Noble. "A review of bayesian networks and structure learning." [[http://www-users.mat.umk.pl/~wniem/SemMgr/BayesNets/Bayesnetsreview.pdf|Mathematica]] Applicanda 40.1 (2012): 51-103. \\ A review from mathematical view point including applications of algebraic geometry to Bayesian networks! |
| - Barabási, Albert-László, Natali Gulbahce, and Joseph Loscalzo. "Network medicine: a network-based approach to human disease. ({{:network_medicine_barabasi_1.pdf|pdf}} )" [[http://www.ncbi.nlm.nih.gov/pubmed/21164525|//Nature Reviews Genetics// ]]12.1 (2011): 56-68. | - Barabási, Albert-László, Natali Gulbahce, and Joseph Loscalzo. "Network medicine: a network-based approach to human disease. ({{:network_medicine_barabasi_1.pdf|pdf}} )" [[http://www.ncbi.nlm.nih.gov/pubmed/21164525|//Nature Reviews Genetics// ]]12.1 (2011): 56-68. |
| - Kustra, Rafal, and Adam Zagdanski. "Incorporating gene ontology in clustering gene expression data." //Computer-Based Medical Systems, 2006. [[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1647629&tag=1|CBMS]] 2006. 19th IEEE International Symposium on//. IEEE, 2006. | - Kustra, Rafal, and Adam Zagdanski. "Incorporating gene ontology in clustering gene expression data." //Computer-Based Medical Systems, 2006. [[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1647629&tag=1|CBMS]] 2006. 19th IEEE International Symposium on//. IEEE, 2006. |
| - Dotan-Cohen, Dikla, Simon Kasif, and Avraham A. Melkman. "Seeing the forest for the trees: using the gene ontology to restructure hierarchical clustering."[[http://www.ncbi.nlm.nih.gov/pubmed/19497934|//Bioinformatics//]] 25.14 (2009): 1789-1795. \\ Semi-supervised clustering. | - Dotan-Cohen, Dikla, Simon Kasif, and Avraham A. Melkman. "Seeing the forest for the trees: using the gene ontology to restructure hierarchical clustering."[[http://www.ncbi.nlm.nih.gov/pubmed/19497934|//Bioinformatics//]] 25.14 (2009): 1789-1795. \\ Semi-supervised clustering. |
| - Hill, Steven M., et al. "Inferring causal molecular networks: empirical assessment through a community-based effort." //[[http://www.nature.com/nmeth/journal/v13/n4/full/nmeth.3773.html?WT.ec_id=NMETH-201604&spMailingID=51038576&spUserID=MTIyMzczNjc4MDI2S0&spJobID=883886888&spReportId=ODgzODg2ODg4S0|Nature]] methods// (2016). \\ A DREAM challenge. Compared 2000 networks in 32 biological contexts. | - Hill, Steven M., et al. "Inferring causal molecular networks: empirical assessment through a community-based effort." //[[http://www.nature.com/nmeth/journal/v13/n4/full/nmeth.3773.html?WT.ec_id=NMETH-201604&spMailingID=51038576&spUserID=MTIyMzczNjc4MDI2S0&spJobID=883886888&spReportId=ODgzODg2ODg4S0|Nature]] methods// (2016). \\ A DREAM challenge. Compared 2000 networks in 32 biological contexts. |
| - Manning, Cerys Sian. Heterogeneity in melanoma and the microenvironment. [[http://discovery.ucl.ac.uk/1381937/1/Thesiswithcorrectionsaccept.pdf|Diss]]. UCL (University College London), 2013. \\ A PhD thesis with a good introduction to melanoma. | - Manning, Cerys Sian. Heterogeneity in melanoma and the microenvironment. [[http://discovery.ucl.ac.uk/1381937/1/Thesiswithcorrectionsaccept.pdf|Diss]]. UCL (University College London), 2013. \\ A PhD thesis with a good introduction to melanoma. |
| - Shannan, Batool, et al. "Heterogeneity in Melanoma." Melanoma. Springer International Publishing, 2016. 1-15 [[[:melanoma_book-2015.pdf?media=melanoma_book-2015.pdf|pdf]]]. \\ A chapter of a comprehensive recent book on melanoma. | - Shannan, Batool, et al. "Heterogeneity in Melanoma." Melanoma. Springer International Publishing, 2016. 1-15 [[:melanoma_book-2015.pdf?media=melanoma_book-2015.pdf|pdf]]]. \\ A chapter of a comprehensive recent book on melanoma. |
| - Jiao, Yinming, Martin Widschwendter, and Andrew E. Teschendorff. "A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control." Bioinformatics 30.16 (2014): 2360-2366. \\ [[https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btu316|FEM]] paper. | - Jiao, Yinming, Martin Widschwendter, and Andrew E. Teschendorff. "A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control." Bioinformatics 30.16 (2014): 2360-2366. \\ [[https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btu316|FEM]] paper. |
| - Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, [[http://www.cell.com/cell/abstract/S0092-8674(17)30639-6?innerTabgraphical_S0092867417306396|Cell]], 2017 [{{:ally-copmprehensive_and_integrative_genomic_char_of_hcc-cell-2017.pdf|pdf}} ]. \\ TCGA's HCC data and subtyping using DNA copy number, DNA methylation, mRNA expression, miRNA expression and RPPA (protein expression). Links to the MDACC dataset with 100 HCC samples. | - Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma, [[http://www.cell.com/cell/abstract/S0092-8674(17)30639-6?innerTabgraphical_S0092867417306396|Cell]], 2017 [{{:ally-copmprehensive_and_integrative_genomic_char_of_hcc-cell-2017.pdf|pdf}} ]. \\ TCGA's HCC data and subtyping using DNA copy number, DNA methylation, mRNA expression, miRNA expression and RPPA (protein expression). Links to the MDACC dataset with 100 HCC samples. |
| - 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). |
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| - See the [[:comparison_of_bayesian_network_learners|table]] for comparison of methods for learning BNs related to our gene network project. | - See the [[:comparison_of_bayesian_network_learners|table]] for comparison of methods for learning BNs related to our gene network project. |
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| \\ \\ [[:gene_networks_inference|Drafts]], [[:gene_networks_inference|Next steps]] | \\ \\ [[:drafts|Drafts]], [[:next_steps|Next steps]] |
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