Connection
Brooke Fridley to Algorithms
This is a "connection" page, showing publications Brooke Fridley has written about Algorithms.
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Connection Strength |
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1.988 |
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Chalise P, Fridley BL. Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm. PLoS One. 2017; 12(5):e0176278.
Score: 0.569
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Fridley BL, Jenkins GD, Grill DE, Kennedy RB, Poland GA, Oberg AL. Soft truncation thresholding for gene set analysis of RNA-seq data: application to a vaccine study. Sci Rep. 2013 Oct 09; 3:2898.
Score: 0.444
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Chalise P, Batzler A, Abo R, Wang L, Fridley BL. Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis. OMICS. 2012 Jul-Aug; 16(7-8):363-73.
Score: 0.406
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Chalise P, Ni Y, Fridley BL. Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization. Comput Biol Med. 2020 03; 118:103625.
Score: 0.172
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Brisbin A, Fridley BL. Bayseian genomic models for the incorporation of pathway topology knowledge into association studies. Stat Appl Genet Mol Biol. 2013 Aug; 12(4):505-16.
Score: 0.110
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Breheny P, Chalise P, Batzler A, Wang L, Fridley BL. Genetic association studies of copy-number variation: should assignment of copy number states precede testing? PLoS One. 2012; 7(4):e34262.
Score: 0.100
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Fridley BL, Lund S, Jenkins GD, Wang L. A Bayesian integrative genomic model for pathway analysis of complex traits. Genet Epidemiol. 2012 May; 36(4):352-9.
Score: 0.100
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Mitchell JR, Szepietowski P, Howard R, Reisman P, Jones JD, Lewis P, Fridley BL, Rollison DE. A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study. J Med Internet Res. 2022 03 23; 24(3):e27210.
Score: 0.050
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Wu D, Yang H, Winham SJ, Natanzon Y, Koestler DC, Luo T, Fridley BL, Goode EL, Zhang Y, Cui Y. Mediation analysis of alcohol consumption, DNA methylation, and epithelial ovarian cancer. J Hum Genet. 2018 Mar; 63(3):339-348.
Score: 0.037
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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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