Daniel Zak
About
Daniel Zak is from Seattle, Washington, United States. Daniel is currently Director, Data Sciences at Regeneron, located in Seattle, WA. In Daniel's previous role as a Director, Data Sciences at 2seventy bio, Daniel worked in Seattle, Washington, United States until Apr 2024. Prior to joining 2seventy bio, Daniel was a Director at bluebird bio and held the position of Director at Seattle, Washington, United States. Prior to that, Daniel was a Associate Director at bluebird bio, based in Seattle, Washington, United States from Jan 2021 to Aug 2021. Daniel started working as Senior Data Scientist at bluebird bio in Seattle, Washington in Jul 2019. From Oct 2017 to Jul 2019, Daniel was Principal Scientist, Computational Biology at Seattle Genetics, based in Bothell, Washington. Prior to that, Daniel was a Affiliate Member at Center for Infectious Disease Research, based in Seattle, Washington from Sep 2017 to Jul 2019. Daniel started working as Assistant Professor at Center for Infectious Disease Research in Greater Seattle Area in Feb 2015.
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Daniel Zak's current jobs
Regeneron Cell Medicines (RCM)
Daniel Zak's past jobs
Bioinformatics Principal Scientist in Translational Diagnostics, Analytics, and Biomarkers for Immuno-Oncology programs. •Integration and machine learning of heterogeneous clinical and molecular datasets from Phase-I/II clinical trials of novel Immune-Oncology agents and combinations to support dosing determination, selection of patients and indications, and understanding of toxicities (for example, Zak et al., ASH 2018). •Mining public tumor transcriptome and single-cell RNA-Seq datasets for deconvolution and interpretation of clinical trial biopsies for mechanism of action analysis and selection of patients and target indications. • Biomarker and Translational Sciences strategy development for new therapeutics entering clinical trials. • Application and development of collaborative and reproducible analytical workflows.
Multi-omics host biomarkers of tuberculosis progression and treatment response (2011-2017) •Machine learning analysis of blood RNA-Seq and qRT-PCR transcriptomes to discover predictive signatures for risk of progressing to active tuberculosis (for example, Zak et al., Lancet, 2016). •The signatures were validated by blind prediction on independent African cohorts and are being evaluated in a prospective “screen and treat” clinical trials. •Extending the data mining approaches to metabolomic (Weiner et al, 2018; Duffy et al., submitted) and proteomic (Scriba et al., 2017) platforms and prediction of treatment failure (Thompson et al., 2017).
Innate control of the vaccine-induced adaptive immunity (2010-2017) •Discovered transcriptional signatures linking the magnitude & phenotype of the innate immune response to the magnitude & phenotype of the adaptive immune response for subunit & vector vaccines. •These signatures were discovered by integration of longitudinal microarray and immune response measurements made in pre-clinical (mouse, NHP) and clinical trials of candidate HIV and licensed Hepatitis B vaccines (Zak et al., PNAS, 2012; Quinn & Zak et al., 2015; Francica & Zak et al., 2017). •The approach has been extended to transcriptomic (RNA-Seq), metabolomic, and proteomic signatures of RhCMV/MTB vaccine-mediated protection (Hansen, Zak, Yu et al., 2018) and efficacy of malaria vaccines.
Innate immune signaling crosstalk in macrophages (2005-2017) •Experimentally and computationally defined transcriptional & signaling circuitry underlying synergy & interference between TLR pathways using large-scale genetic & pharmacological perturbations. •These analyses identified novel modulators of the inflammatory response, including SHARPIN (Zak et al., 2012) and have been extended to analysis of specificity in the macrophage secreted metabolome.