M
Michael Snyder
Researcher at Stanford University
Publications - 938
Citations - 150929
Michael Snyder is an academic researcher from Stanford University. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 169, co-authored 840 publications receiving 130225 citations. Previous affiliations of Michael Snyder include Wyss Institute for Biologically Inspired Engineering & Public Health Research Institute.
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Journal ArticleDOI
Hummingbird: Efficient Performance Prediction for Executing Genomic Applications in the Cloud.
Amir Bahmani,Amir Bahmani,Ziye Xing,Vandhana Krishnan,Utsab Ray,Frank Mueller,Amir H. Alavi,Philip S. Tsao,Michael Snyder,Michael Snyder,Cuiping Pan +10 more
TL;DR: Hummingbird as mentioned in this paper predicts the fastest, the cheapest, and the most cost-efficient compute instances in an economic manner for genomic data pipelines on multiple cloud platforms, such as GATK HaplotypeCaller, GATk MuTect2, and ENCODE ATAC-seq.
Journal ArticleDOI
RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data.
TL;DR: It is found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples.
Journal ArticleDOI
Corrigendum: Secure cloud computing for genomic data.
TL;DR: In the version of this article initially published, the competing financial interests line should have been positive in the HTML as it was in the PDF (see as mentioned in this paper for a corrected version).
Posted ContentDOI
Multiomics Longitudinal Modeling of Preeclamptic Pregnancies
Ivana Maric,Kévin Contrepois,Mira N. Moufarrej,Ina A. Stelzer,Dorien Feyaerts,Xiaoyuan Han,Andy Tang,Natalie Stanley,Ronald J. Wong,Gavin M. Traber,Mathew Ellenberger,Alan Chang,Ramin Fallahzadeh,Huda Nassar,Martin Becker,Maria Xenochristou,Camilo Espinosa,Davide De Francesco,Mohammad Sajjad Ghaemi,Elizabeth K. Costello,Anthony Culos,Xuefend B. Ling,Karl G. Sylvester,Gary L. Darmstadt,Virginia D. Winn,Gary M. Shaw,David A. Relman,Stephen R. Quake,Martin S. Angst,Michael Snyder,David K. Stevenson,Brice Gaudilliere,Nima Aghaeepour +32 more
TL;DR: Preeclampsia risk models were developed by analyzing six omics datasets from a longitudinal cohort of pregnant women and integrated with immune system cytometry data, confirming known physiological alterations associated with preeClampsia and suggesting novel associations between the immune and proteomic dynamics.
Journal ArticleDOI
A new journal for the post-genome era
TL;DR: Functional genomics is characterised by methodologies involving high throughput and subsequent computational analysis, which are directed at the gene level or the protein level (proteomics), which has great potential for medical application of functional data in the understanding of disease and the search for remedies.