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Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data., Phillipe Loher, Nestoras Karathanasis
Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data., Phillipe Loher, Nestoras Karathanasis
Computational Medicine Center Faculty Papers
The development of single-cell sequencing technologies has allowed researchers to gain important new knowledge about the expression profile of genes in thousands of individual cells of a model organism or tissue. A common disadvantage of this technology is the loss of the three-dimensional (3-D) structure of the cells. Consequently, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized the Single-Cell Transcriptomics Challenge, in which we participated, with the aim to address the following two problems: (a) to identify the top 60, 40, and 20 genes of the Drosophila melanogaster embryo that contain the most spatial information and (b) to …