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Connection

Co-Authors

This is a "connection" page, showing publications co-authored by Peter Carbonetto and Matthew Stephens.
Connection Strength

4.405
  1. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. Genome Biol. 2023 10 19; 24(1):236.
    View in: PubMed
    Score: 0.843
  2. Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease. PLoS Genet. 2013; 9(10):e1003770.
    View in: PubMed
    Score: 0.420
  3. fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles. bioRxiv. 2025 Aug 17.
    View in: PubMed
    Score: 0.239
  4. BAYESIAN VARIABLE SELECTION IN A COX PROPORTIONAL HAZARDS MODEL WITH THE "SUM OF SINGLE EFFECTS" PRIOR. ArXiv. 2025 Jun 06.
    View in: PubMed
    Score: 0.236
  5. Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition. Nat Genet. 2025 Jan; 57(1):263-273.
    View in: PubMed
    Score: 0.229
  6. Accelerated dimensionality reduction of single-cell RNA sequencing data with fastglmpca. Bioinformatics. 2024 08 02; 40(8).
    View in: PubMed
    Score: 0.223
  7. Accelerated dimensionality reduction of single-cell RNA sequencing data with fastglmpca. bioRxiv. 2024 Jul 04.
    View in: PubMed
    Score: 0.221
  8. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. bioRxiv. 2024 Jun 18.
    View in: PubMed
    Score: 0.221
  9. Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition. bioRxiv. 2024 May 26.
    View in: PubMed
    Score: 0.220
  10. A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes. PLoS Genet. 2023 07; 19(7):e1010539.
    View in: PubMed
    Score: 0.207
  11. Fine-mapping from summary data with the "Sum of Single Effects" model. PLoS Genet. 2022 07; 18(7):e1010299.
    View in: PubMed
    Score: 0.193
  12. Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage. J Mach Learn Res. 2021 Jan-Dec; 22.
    View in: PubMed
    Score: 0.174
  13. A simple new approach to variable selection in regression, with application to genetic fine mapping. J R Stat Soc Series B Stat Methodol. 2020 Dec; 82(5):1273-1300.
    View in: PubMed
    Score: 0.168
  14. Creating and sharing reproducible research code the workflowr way. F1000Res. 2019; 8:1749.
    View in: PubMed
    Score: 0.160
  15. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat Genet. 2019 01; 51(1):187-195.
    View in: PubMed
    Score: 0.150
  16. Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. Heredity (Edinb). 2019 03; 122(3):261-275.
    View in: PubMed
    Score: 0.146
  17. Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet. 2013; 9(2):e1003264.
    View in: PubMed
    Score: 0.100
  18. Genetic contributions to epigenetic-defined endotypes of allergic phenotypes in children. Am J Hum Genet. 2025 Jul 03; 112(7):1610-1624.
    View in: PubMed
    Score: 0.059
  19. Genetic contributions to epigenetic-defined endotypes of allergic phenotypes in children. medRxiv. 2024 Oct 04.
    View in: PubMed
    Score: 0.056
  20. A pairwise cytokine code explains the organism-wide response to sepsis. Nat Immunol. 2024 Feb; 25(2):226-239.
    View in: PubMed
    Score: 0.053
  21. Fine-mapping studies distinguish genetic risks for childhood- and adult-onset asthma in the HLA region. Genome Med. 2022 05 24; 14(1):55.
    View in: PubMed
    Score: 0.048
  22. A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming. J Comput Graph Stat. 2020; 29(2):261-273.
    View in: PubMed
    Score: 0.041
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.