ISB-CGC Publications

  1. Sheila M. Reynolds, Michael Miller, Phyliss Lee, et al. The ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research. Cancer Res, 2017. doi: 10.1158/0008-5472.CAN-17-0617.
  2. Kawther Abdilleh, Boris Aguilar, J. Ross Thomson et al. Multi-omics data integration in the Cloud: Analysis of Statistically Significant Associations Between Clinical and Molecular Features in Breast Cancer. 2020. doi: 10.1145/3388440.3414917.
  3. Dondra Bailey, Kawther Abdilleh, Boris Aguilar, et al. Multi-omics characterization of Microtubule-actin cross linking factor 1 (MACF1) using the ISB-Cancer Genomics Cloud. 2020. doi: 10.1145/3388440.3414918.
  4. Kawther Abdilleh, Boris Aguilar, Ronald C. Taylor, et al. Large-scale Cloud-based Inference of Differential Breast Cancer-related Network Gene Between Patient Cohorts. 2020.
  5. Aguilar B, Gibbs DL, Reiss DJ, et al. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. 2020.
  6. Gibbs DL, Aguilar B, Thorsson V, Ratushny AV, Shmulevich I. Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer. Frontiers in Genetics. 2021;12:1489. doi:10.3389/fgene.2021.667382.
  7. Boris Aguilar, Kawther Abdilleh, George Acquaah-Mensah. A tale of two cohorts: Transcriptomics and epigenomic analysis in breast cancer. 2021.
  8. Kawther Abdilleh, Boris Aguilar, Ronald C. Taylor, et al. Multi-omics data analysis in the cloud: inference of differential breast cancer-related network hubs between TCGA patient cohorts. 2021. doi: 10.7490/f1000research.1118296.1.
  9. Plaugher D, Aguilar B, Murrugarra D. Uncovering potential interventions for pancreatic cancer patients via mathematical modeling. 2022. doi: 10.1101/2022.01.11.475711.
  10. de Andrade KC, Lee EE, Tookmanian EM, et al. The TP53 Database: transition from the International Agency for Research on Cancer to the US National Cancer Institute. 2022. doi: 10.1038/s41418-022-00976-3.
  11. Tercan B, Qin G, Kim TK, et al. SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery. 2022. doi:10.12688/f1000research.110903.2.
  12. Wang J, Zheng J, Lee E, et al. A cloud-based resource for genome coordinate-based exploration and large-scale analysis of chromosome aberrations and gene fusions in cancer. 2023. doi:10.1002/gcc.23128.
  13. Torcivia J, Abdilleh K, Seidl F, et al. Whole Genome Variant Dataset for Enriching Studies across 18 Different Cancers. 2023. doi:10.3390/onco2020009.
  14. Pot D, Worman Z, Baumann A, et al. NCI Cancer Research Data Commons: Cloud-based Analytical Resources. Cancer Research. 2024. doi:10.1158/0008-5472.CAN-23-2657
  15. Seidl F, Hagen L, Wilson J, et al. The ISB Cancer Gateway in the Cloud (ISB-CGC): Access, explore and analyze large-scale cancer data through the Google Cloud. Cancer Research. 2024. doi:10.1158/1538-7445.AM2024-3547
  16. Kawther Abdilleh, Boris Aguilar, George Acquaah-Mensah Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches . Clinical Breast Cancer, 2024. doi:10.1016/j.clbc.2024.11.015
  17. Gibbs DL, Cioffi G, Aguilar B, et al. Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM. Cancers, 2025. doi:10.3390/cancers17030445

ISB-CGC Blogs

  1. Bleich D, Wilson J. New Notebook Demonstrates Machine Learning in Google BigQuery Using Updated Mitelman Database. 2024.
  2. Thomson R. How to run statistics inside BigQuery. 2023.
  3. Bleich D. How the Mitelman Database Can Help You Explore Genomic Abnormalities. 2023.
  4. Bleich D. ISB-CGC Cloud Resource: Providing Researchers with Shortcuts to Data Analysis. 2022.
  5. Bleich D, Kuan A, Pot D, Ray M, Subramanian SL, Van der Auwera G. NCI’s Cloud Resources Help Tame Today’s Data Windfall. 2021.

Citations

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