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Connection

Hae Kyung Im to Models, Genetic

This is a "connection" page, showing publications Hae Kyung Im has written about Models, Genetic.
Connection Strength

1.305
  1. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018 05 08; 9(1):1825.
    View in: PubMed
    Score: 0.380
  2. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLoS Genet. 2016 Nov; 12(11):e1006423.
    View in: PubMed
    Score: 0.342
  3. RatXcan: A framework for cross-species integration of genome-wide association and gene expression data. PLoS Genet. 2025 Mar; 21(3):e1011583.
    View in: PubMed
    Score: 0.153
  4. Protein prediction for trait mapping in diverse populations. PLoS One. 2022; 17(2):e0264341.
    View in: PubMed
    Score: 0.123
  5. sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome Biol. 2020 09 11; 21(1):235.
    View in: PubMed
    Score: 0.112
  6. Poly-omic prediction of complex traits: OmicKriging. Genet Epidemiol. 2014 Jul; 38(5):402-15.
    View in: PubMed
    Score: 0.072
  7. A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction. Nat Commun. 2021 03 03; 12(1):1424.
    View in: PubMed
    Score: 0.029
  8. CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses. Nat Commun. 2020 08 21; 11(1):4208.
    View in: PubMed
    Score: 0.028
  9. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science. 2019 10 18; 366(6463):351-356.
    View in: PubMed
    Score: 0.026
  10. Genetic architecture of gene expression traits across diverse populations. PLoS Genet. 2018 08; 14(8):e1007586.
    View in: PubMed
    Score: 0.024
  11. Genetic architecture of microRNA expression: implications for the transcriptome and complex traits. Am J Hum Genet. 2012 Jun 08; 90(6):1046-63.
    View in: PubMed
    Score: 0.016
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.