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10.1093/gigascience/giaa064 http://scihub22266oqcxt.onion/10.1093/gigascience/giaa064 32543653!7297091!32543653     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Gigascience 2020 ; 9 (6): ä Nephropedia Template TP {{tp|p=32543653|t=2020. Integrative computational epigenomics to build data-driven gene regulation hypotheses |pdf=|usr=}}{{32543653}} gab.com Text Integrative computational epigenomics to build data-driven gene regulation hypotheses JO: Gigascience http://www.kidney.de/pget.php?&tw=1&pmid=32543653 #FOAvip BACKGROUND: Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an array of laboratory assays from multiple modalities. Often, each of these resulting datasets is large, heterogeneous, and noisy. Thus, it is non-trivial to unify these complex datasets into an interpretable phenotype. Although recent methods address this problem with varying degrees of success, they are constrained by their scopes or limitations. Therefore, an important gap in the field is the lack of a universal data harmonizer with the capability to arbitrarily integrate multi-modal datasets. RESULTS: In this review, we perform a critical analysis of methods with the explicit aim of harmonizing data, as opposed to case-specific integration. This revealed that matrix factorization, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonization framework. CONCLUSIONS: A sufficiently advanced universal harmonizer has major medical implications, such as (i) identifying dysregulated biological pathways responsible for a disease is a powerful diagnostic tool; (2) investigating these pathways further allows the biological community to better understand a disease's mechanisms; and (3) precision medicine also benefits from developments in this area, particularly in the context of the growing field of selective epigenome editing, which can suppress or induce a desired phenotype. Twit Text FOAVip Integrative computational epigenomics to build data-driven gene regulation hypotheses ????Gigascience http://www.kidney.de/pget.php?&tw=1&pmid=32543653 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32543653 #FOAvip English Wikipedia <ref>{{Cite pmid|32543653}}</ref> Integrative computational epigenomics to build data-driven gene regulation hypotheses #MMPMID32543653 Chen T; Tyagi S Gigascience 2020[Jun]; 9 (6): ä PMID32543653show ga BACKGROUND: Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an array of laboratory assays from multiple modalities. Often, each of these resulting datasets is large, heterogeneous, and noisy. Thus, it is non-trivial to unify these complex datasets into an interpretable phenotype. Although recent methods address this problem with varying degrees of success, they are constrained by their scopes or limitations. Therefore, an important gap in the field is the lack of a universal data harmonizer with the capability to arbitrarily integrate multi-modal datasets. RESULTS: In this review, we perform a critical analysis of methods with the explicit aim of harmonizing data, as opposed to case-specific integration. This revealed that matrix factorization, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonization framework. CONCLUSIONS: A sufficiently advanced universal harmonizer has major medical implications, such as (i) identifying dysregulated biological pathways responsible for a disease is a powerful diagnostic tool; (2) investigating these pathways further allows the biological community to better understand a disease's mechanisms; and (3) precision medicine also benefits from developments in this area, particularly in the context of the growing field of selective epigenome editing, which can suppress or induce a desired phenotype. |*Epigenesis, Genetic[MESH] |*Gene Expression Regulation[MESH] |Computational Biology/*methods[MESH] |Epigenomics/*methods[MESH] |High-Throughput Nucleotide Sequencing[MESH] |Machine Learning[MESH]Integrative computational epigenomics to build data-driven gene regula(epmc).pdf DeepDyve Pubget Overpricing