Microarray Data Analysis and Gene Expression Profiling

Displaying 1 - 10 of 10CSV
Stear, B. J., Mohseni Ahooyi, T., Simmons, J. A., Kollar, C., Hartman, L., Beigel, K., Lahiri, A., Vasisht, S., Callahan, T. J., Nemarich, C. M., Silverstein, J. C., & Taylor, D. M. (2024). Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data. Scientific Data, 11(1). https://doi.org/10.1038/s41597-024-04070-w
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Yang, M., Matan-Lithwick, S., Wang, Y., De Jager, P. L., Bennett, D. A., & Felsky, D. (2023). Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Communications, 5(2). https://doi.org/10.1093/braincomms/fcad110
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Malamon, J. S., Farrell, J. J., Xia, L. C., Dombroski, B. A., Das, R. G., Way, J., Kuzma, A. B., Valladares, O., Leung, Y. Y., Scanlon, A. J., Lopez, I. A. B., Brehony, J., Worley, K. C., Zhang, N. R., Wang, L.-S., Farrer, L. A., Schellenberg, G. D., Lee, W.-P., & Vardarajan, B. N. (2024). A comparative study of structural variant calling in WGS from Alzheimer’s disease families. Life Science Alliance, 7(5), e202302181. https://doi.org/10.26508/lsa.202302181
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Kasela, S., Aguet, F., Kim-Hellmuth, S., Brown, B. C., Nachun, D. C., Tracy, R. P., Durda, P., Liu, Y., Taylor, K. D., Johnson, W. C., Van Den Berg, D., Gabriel, S., Gupta, N., Smith, J. D., Blackwell, T. W., Rotter, J. I., Ardlie, K. G., Manichaikul, A., Rich, S. S., … Lappalainen, T. (2024). Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. The American Journal of Human Genetics, 111(1), 133–149. https://doi.org/10.1016/j.ajhg.2023.11.013
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Zhong, G., Choi, Y. A., & Shen, Y. (2023). VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants. Communications Biology, 6(1). https://doi.org/10.1038/s42003-023-05155-9
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FitzPatrick, V. D., Leemans, C., van Arensbergen, J., van Steensel, B., & Bussemaker, H. J. (2023). Defining the fine structure of promoter activity on a genome-wide scale with CISSECTOR. Nucleic Acids Research, 51(11), 5499–5511. https://doi.org/10.1093/nar/gkad232
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Ghandhi, S. A., Shuryak, I., Ponnaiya, B., Wu, X., Garty, G., Morton, S. R., Kaur, S. P., & Amundson, S. A. (2022). Cross-platform validation of a mouse blood gene signature for quantitative reconstruction of radiation dose. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-18558-1
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Lakkis, J., Schroeder, A., Su, K., Lee, M. Y. Y., Bashore, A. C., Reilly, M. P., & Li, M. (2022). A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation. Nature Machine Intelligence, 4(11), 940–952. https://doi.org/10.1038/s42256-022-00545-w
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