 Connection
Casey Greene to Sequence Analysis, RNA
This is a "connection" page, showing publications Casey Greene has written about Sequence Analysis, RNA.
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Connection Strength |
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1.487 |
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Hu Q, Greene CS. Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics. Pac Symp Biocomput. 2019; 24:362-373.
Score: 0.517
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Cheng C, Moore J, Greene C. Applications of bioinformatics to non-coding RNAs in the era of next-generation sequencing. Pac Symp Biocomput. 2014; 412-6.
Score: 0.366
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Ivich A, Davidson NR, Grieshober L, Li W, Hicks SC, Doherty JA, Greene CS. Missing cell types in single-cell references impact deconvolution of bulk data but are detectable. Genome Biol. 2025 Apr 07; 26(1):86.
Score: 0.200
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Hippen AA, Omran DK, Weber LM, Jung E, Drapkin R, Doherty JA, Hicks SC, Greene CS. Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors. Genome Biol. 2023 10 20; 24(1):239.
Score: 0.180
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Hippen AA, Falco MM, Weber LM, Erkan EP, Zhang K, Doherty JA, V?h?rautio A, Greene CS, Hicks SC. miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data. PLoS Comput Biol. 2021 08; 17(8):e1009290.
Score: 0.155
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Foltz SM, Greene CS, Taroni JN. Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously. Commun Biol. 2023 02 25; 6(1):222.
Score: 0.043
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Rudd J, Zelaya RA, Demidenko E, Goode EL, Greene CS, Doherty JA. Leveraging global gene expression patterns to predict expression of unmeasured genes. BMC Genomics. 2015 Dec 15; 16:1065.
Score: 0.026
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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