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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  7. Ahmad, S., Khan, F.N., Ramlal, A., Begum, S., Qazi, S. and Raza, K. (2023) “Chapter 11 - Nanoinformatics and nanomodeling: Recent developments in computational nanodrug design and delivery systems,” in N. Ahmad and G. Packirisamy (eds.) Emerging Nanotechnologies for Medical Applications. Elsevier (Micro and Nano Technologies), pp. 297–332. Available at: https://doi.org/10.1016/B978-0-323-91182-5.00001-2.
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  12. Akkari, Y., Baughn, L.B., Kim, A., Karaca, E., Raca, G., Shao, L. and Mikhail, F.M. (2024) “Section E6.1–6.6 of the American College of Medical Genetics and Genomics (ACMG) Technical Laboratory Standards: Cytogenomic studies of acquired chromosomal abnormalities in neoplastic blood, bone marrow, and lymph nodes,” Genetics in Medicine, p. 101054. Available at: https://doi.org/10.1016/j.gim.2023.101054.
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  20. Ananthaneni, A., Benzar, T., Hafiz, N. and Akabane, H. (2023) “De novo monocytic-M5b AML with t(8;16) (p11.2; p13.3) KAT6A/CREBBP fusion and FLT3-TKD mutation complicated by chemotherapy-induced Takotsubo cardiomyopathy,” BMJ Case Reports CP, 16(3), p. e253812. Available at: https://doi.org/10.1136/bcr-2022-253812.
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  22. Andrew, B., Attilio, O., Sumit, G., Vijay, T., Reshad, G.S. and Osvaldo, P. (2021) “A rare case of B cell lymphoblastic leukemia with inv(7)(p15q34) with review of literature.,” Leukemia Research Reports, 15, p. 100250. Available at: https://doi.org/10.1016/j.lrr.2021.100250.
  23. Anitha, D.C., Gnanatheepa, K.T.C., Hubert, G., Yadav, R., Naik, S.B. and Singh, B. (2022) “AI BASED HERBAL TREATMENT FOR CANCER CELL,” Journal of Pharmaceutical Negative Results, pp. 7623–7636. Available at: https://doi.org/10.47750/pnr.2022.13.S07.920.
  24. Arbajian, E., Hofvander, J., Magnusson, L. and Mertens, F. (2020) “Deep sequencing of myxoinflammatory fibroblastic sarcoma,” Genes Chromosomes and Cancer, 59(5), pp. 309–317. Available at: https://doi.org/10.1002/gcc.22832.
  25. Arnon, J., Zick, A., Maoz, M., Salaymeh, N., Gugenheim, A., Marouani, M., Mor, E., Hamburger, T., Saadi, N., Elia, A., Ganz, G., Fahham, D., Meirovitz, A., Kadouri, L., Meiner, V., Yablonski-Peretz, T. and Shkedi-Rafid, S. (2024) “Clinical and genetic characteristics of carriers of the TP53 c.541C > T, p.Arg181Cys pathogenic variant causing hereditary cancer in patients of Arab-Muslim descent,” Familial Cancer [Preprint]. Available at: https://doi.org/10.1007/s10689-024-00391-2.
  26. Avenarius, M.R., Abrams, Z.B., Guo, L., Blachly, J.S., Miller, C.R., Heerema, N.A., Tang, G., Coombes, K.R. and Abruzzo, L.V. (2025) “Novel Recurrent Cytogenetic Abnormalities Predict Overall Survival in Tetraploid/Near-Tetraploid Myelodysplastic Syndrome and Acute Myeloid Leukemia,” Cancers, 17(8), p. 1277. Available at: https://doi.org/10.3390/cancers17081277.
  27. Avgerinou, G., Stefanaki, K., Liapis, K., Kostopoulos, I.V., Kossiva, L., Tzoumaka-Bakoula, C., Pavlidis, D., Filippidou, M., Katsibardi, K., Ampatzidou, M., Kattamis, A., Polychronopoulou, S., Mantzourani, M. and Papadhimitriou, S.I. (2021) “Fish evaluation of additional cytogenetic aberrations and hyperdiploidy in childhood Burkitt lymphoma,” Leukemia & Lymphoma, 0(0), pp. 1–11. Available at: https://doi.org/10.1080/10428194.2021.1998480.
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