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APPLICATIONS IN CANCER. “A particularly compelling set of use cases for the application of systems biology to understand the genomics of disease can be found in cancer. Efforts to characterize the most prevalent mutations of various cancers by The Cancer Genome Atlas (TCGA) Research Network have revealed recurrent mutations in specific pathways.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

PREDICTIVE DATA. “Adopting methods from systems biology to marshall the data into tractable, predictive models can shed light on the contributions of these individual variants to the phenotypes under study.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

HARD PREDICTIONS. “We are still unable to assess the contribution of these changes in functional elements to what are often complex phenotypes that arise from these combinatorial interactions between multiple variants occurring jointly at genes, let alone their interactions with the environment.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

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MARKER UNCERTAINTIES. “Yet most of these variants are in non-coding regions and we are often not even certain of the association of the variant with an actual gene.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

PROBABILITY MARKERS. “We currently lack the capability to evaluate the impact of most sequence variants found and what their functional consequences are. A decade of Genome-Wide Association Studies have revealed a multitude of common variants associated with various traits and diseases, each of which seems to contribute to or at least to increase the probability of a phenotype by a small amount.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

PERSONALIZED MEDICINE. “The rapid availability of ubiquitous sequencing holds great promise for medicine to the extent that genomics empowers the analysis of patient genomes to guide personalized treatment. While we can now sequence an individual's genome and transcriptomes, it remains extremely difficult to use that data to inform treatment.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

NEW TOOLS. “There are now a multitude of implementations based on this concept that introduce additional functional data such as protein interaction data, gene regulatory networks, pathway topology information, metabolic changes or expression kinetics. These methods have been applied not only to understand gene expression changes but also in Genome-Wide Association Studies (GWAS), comparative genomics and gene prioritization.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

RANKED NOT SELECTED. “The first functional enrichment analysis methods identified pathways that were overrepresented within a list of differentially expressed genes and were rapidly followed by the gene set enrichment approach where a ranked, rather than a selected, list of genes was used to find associations between phenotypes and cellular functions.” https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S1 View in LinkedIn

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