Elizabeth Brunk

Multi-omics ・ Computational Biology ・ Machine Learning

Omics-guided Design Strategies for Therapeutic Proteins

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Variant Interpretation ・ Drug Response Pathways ・ Therapeutic Protein Design


[1] Sastry, A, et al. Bioinformatics (2017)
[2] Brunk, E, et al.  Cell Systems (2016) 2 (5): 335
[3] Lechner, A; Brunk, E; Keasling, JD. Cold Spring Harbor Perspectives (2016)
[4] Brunk, E, et. al. chemphyschem (2016)
[5] Bozkurt, E, et al. CHIMIA (2014) 68 (9) 642
[6] Ebrahim, A*; Brunk, E*, et al. Nature Communications (2016)
[7] Latif, H, et al. Biotechniques (2015) 58, 329
[8] Brunk, E*, et al. BMC systems biology (2016) 10(1):26
[9] Guzman, G, et al. PNAS (2015) 112 (3) 929-934
[10] Mcklosky, D. et al. Metabolic Engineering (2018)

<< back to Research Interests >>
Variant Interpretation ・ Drug Response Pathways ・ Therapeutic Protein Design


Key words: genomics, proteomics, metabolomics, structural biology, pharmacogenomics, big data, variant interpretation, mutation analysis, high-throughput sequencing, precision medicine, genomic medicine, preventative genomics, systems medicine, systems biology, structural proteome, variome, variomics, trans-omics, vertical integration, horizontal integration, multi-omics data integration, big data to knowledge, heterologous protein production, synthetic biology, recombinant protein, human protein atlas, machine learning, deep learning, probabilistic analysis, inference analysis, data reduction, decomposition, feature selection, genetic algorithm, knowledge-based approach, biomimetic design, evolutionary algorithm

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