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SARS-CoV-2 RNA polymerase (nsp12, RdRP) dataset: A 3.4 ms dataset of the SARS-CoV-2 nsp12 protein in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 nsp9 simulations: A 9 ms dataset of the SARS-CoV-2 nsp9 protein in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 nsp8 simulations: A 1.8 ms dataset of the SARS-CoV-2 nsp8 protein in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 nsp7 simulations: A 3.7 ms dataset of the SARS-CoV-2 nsp7 protein in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 nsp3 pl2pro domain dataset: An 731 µs dataset of the SARS-CoV-2 nsp3 pl2pro domain in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 nsp3 macrodomain dataset: An 11 ms dataset of the SARS-CoV-2 nsp3 macrodomain in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 nsp10 dataset: A 6.1 ms dataset of the SARS-CoV-2 nsp10 protein in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) monomer simulations: A 6.4 ms dataset of the SARS-CoV-2 main viral protease (apo, monomer) in search of cryptic pockets by The Bowman lab at Washington University in St. SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) monomer simulations: A 2.6 ms equilibrium dataset of the SARS-CoV-2 main viral protease (apo, monomer) by The Chodera lab at MSKCC. SARS-CoV-2 main viral protease (Mpro, 3CLPro, nsp5) dimer simulations: A 2.9 ms dataset of the SARS-CoV-2 main viral protease (apo, dimer) in search of cryptic pockets by The Bowman lab at Washington University in St. 
Amazon mical tur jobs free#
SARS-CoV-2 COVID Moonshot absolute free energy calculations by The Voelz lab at Temple University.
SARS-CoV-2 spike RBD with N501Y mutation bound to human ACE2 (953.7 µs) by The Chodera lab at the Memorial Sloan Kettering Cancer Center.Gibbs, et al.įoldingathome COVID-19 Datasets Tutorials The Immune Landscape of Cancer by Vésteinn Thorsson, David L.The chromatin accessibility landscape of primary human cancers by M.Spatial Organization And Molecular Correlation Of Tumor-Infiltrating Lymphocytes Using Deep.Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomicīy Kyle Ellrott, Matthew H.Pathogenic Germline Variants in 10,389 Adult Cancers by Kuan-lin Huang, R.Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Eachīy Hua-Sheng Chiu, Sonal Somvanshi, et al.Pan-Cancer Alterations of the MYC Oncogene and its Proximal Network Across The Cancer Genomeīy Franz X.Oncogenic Signaling Pathways in The Cancer Genome Atlas by Francisco Sanchez-Vega, Marco Mina, et al.Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human.Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiationīy Tathiane M.Machine Learning Detects Pan-Cancer Ras Pathway Activation in The Cancer Genome Atlas by Gregory P.Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types by Zhongqi Ge, Jake S.Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas by Joshua D.Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genomeīy Theo A.Genomic and Functional Approaches to Understanding Cancer Aneuploidy by Alison M.
Amazon mical tur jobs driver#
Comprehensive Characterization of Cancer Driver Genes and Mutations by Matthew H. Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients by André Kahles, Kjong-Van Lehmann, et al. Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas by Yang Liu, Nilay S. Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Typesīy Katherine A. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcomeīy Jianfang Liu, Tara Lichtenberg, et al. A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples by Han Chen, Chunyan Li, et al. "Before and After: A Comparison of Legacy and Harmonized TCGA Data at the Genomic Data. Using TCGA Data, Resources, and Materials by National Cancer Institute. TCGA Cancers Selected for Study by National Cancer Institute. ISB Cancer Genomics Cloud by Institute for Systems Biology. Genomic Data Commons by National Cancer Institute. Amazon mical tur jobs archive#
GDC Legacy Archive by National Cancer Institute. Broad Institute FireCloud by The Broad Institute of MIT & Harvard. The Cancer Genome Atlas Tools & Applications
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