Zobrazeno 1 - 10
of 240
pro vyhledávání: '"GARMIRE, LANA X."'
Autor:
Huang, Yu-Ning, Love, Michael I., Ronkowski, Cynthia Flaire, Deshpande, Dhrithi, Schriml, Lynn M., Wong-Beringer, Annie, Mons, Barend, Corbett-Detig, Russell, Hunter, Christopher I, Moore, Jason H., Garmire, Lana X., Reddy, T. B. K., Hide, Winston A., Butte, Atul J., Robinson, Mark D., Mangul, Serghei
Metadata, often termed "data about data," is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpretation, enabling users to access, comprehend, and utilize data effecti
Externí odkaz:
http://arxiv.org/abs/2401.02965
Autor:
Garmire, Lana X., Li, Yijun, Huang, Qianhui, Xu, Chuan, Teichmann, Sarah, Kaminski, Naftali, Pellegrini, Matteo, Nguyen, Quan, Teschendorff, Andrew E.
Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances from a variety of omics data. D
Externí odkaz:
http://arxiv.org/abs/2211.11808
Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods
Externí odkaz:
http://arxiv.org/abs/2203.09664
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. However, the pr
Externí odkaz:
http://arxiv.org/abs/2201.06725
Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free
Externí odkaz:
http://arxiv.org/abs/2109.11746
Autor:
He, Bing, Xiao, Yao, Liang, Haodong, Huang, Qianhui, Du, Yuheng, Li, Yijun, Garmire, David, Sun, Duxin, Garmire, Lana X.
Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for prec
Externí odkaz:
http://arxiv.org/abs/2109.06377
Cox-nnet v2.0: improved neural-network based survival prediction extended to large-scale EMR dataset
Cox-nnet is a neural-network based prognosis prediction method, originally applied to genomics data. Here we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis b
Externí odkaz:
http://arxiv.org/abs/2009.04412
Autor:
Garmire, Lana X
Genomics, especially multi-omics, has made precision medicine feasible. The completion and publicly accessible multi-omics resource with clinical outcome, such as The Cancer Genome Atlas (TCGA) is a great test bed for developing computational methods
Externí odkaz:
http://arxiv.org/abs/2008.12455
Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the hetero
Externí odkaz:
http://arxiv.org/abs/2008.03380
Autor:
Brito, Jaqueline J., Li, Jun, Moore, Jason H., Greene, Casey S., Nogoy, Nicole A., Garmire, Lana X., Mangul, Serghei
Computational methods have reshaped the landscape of modern biology. While the biomedical community is increasingly dependent on computational tools, the mechanisms ensuring open data, open software, and reproducibility are variably enforced by acade
Externí odkaz:
http://arxiv.org/abs/2001.05127