Searching journal content for articles similar to Huynen et al. 10 (8): 1204.

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  1. Method: Autoencoders for genomic variation analysis
    • Margarita Geleta,
    • Daniel Mas Montserrat,
    • Xavier Giro-i-Nieto,
    • and Alexander G. Ioannidis
    Genome Res. February 2026 36: 348-360; Published in Advance January 20, 2026, doi:10.1101/gr.280086.124
    ...conducted both qualitative and quantitative assessments of the quality of VAE clusters on human and canine SNP data sets, reinforcing the benefits of VAE for dimensionality reduction in a population genetics context. Most notably, we have developed a novel VAE-based lossless compression system tailored...
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  2. Method: Automated interpretable artificial intelligence genomic prediction with AIGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. April 2026 36: 814-826; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ....Model interpretabilityIn the context of GP, SHAP treats each SNP marker (coded as 0/1/2) as an independent contributor to the model output, thereby linking ML predictions directly back to the underlying genotype matrix. By calculating the contribution of each SNP locus to the prediction outcome, SHAP quantifies its...
  3. Method: A spectral component approach leveraging identity-by-descent graphs to address recent population structure in genomic analysis
    • Ruhollah Shemirani,
    • Gillian M. Belbin,
    • Sinead Cullina,
    • Christa Caggiano,
    • Christopher R. Gignoux,
    • Noah Zaitlen,
    • and Eimear E. Kenny
    Genome Res. March 2026 36: 534-546; Published in Advance December 23, 2025, doi:10.1101/gr.280659.125
    ...heritable effects.Evaluating performance for GWASTo evaluate how SPCs perform in the context of GWAS, we conducted GWAS using all seven phenotypes, focusing on the distribution of Z-scores and the inflation of P-values as a measure of the unadjusted effects of population structure (Fig. 3C; Devlin...
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  4. Method: Quantitative prediction of enhancer–promoter interactions
    • Polina S. Belokopytova,
    • Miroslav A. Nuriddinov,
    • Evgeniy A. Mozheiko,
    • Daniil Fishman,
    • and Veniamin Fishman
    Genome Res. January 2020 30: 72-84; Published in Advance December 2, 2019, doi:10.1101/gr.249367.119
    ..., these approaches (1) do not infer EP interactions, (2) perform qualitative, rather than quantitative, prediction (i.e., classification), and (3) for most of them, significantly more input information is required than for 3DPredictor.There are multiple computation tools designed specifically to infer EP...
  5. Research: Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators
    • David van Dijk,
    • Eilon Sharon,
    • Maya Lotan-Pompan,
    • Adina Weinberger,
    • Eran Segal,
    • and Lucas B. Carey
    Genome Res. January 2017 27: 87-94; Published in Advance December 13, 2016, doi:10.1101/gr.212316.116
    ...-change in expression at low and high [AA] and no change in the dynamic range of the promoter (Fig. 2C,I–K). Adding a binding site for a repressor, changing the position of the binding site, or changing the promoter sequence context to a context with a different predicted nucleosome occupancy also results in no change...
  6. Method: Predicting genetic modifier loci using functional gene networks
    • Insuk Lee,
    • Ben Lehner,
    • Tanya Vavouri,
    • Junha Shin,
    • Andrew G. Fraser,
    • and Edward M. Marcotte
    Genome Res. August 2010 20: 1143-1153; Published in Advance June 9, 2010, doi:10.1101/gr.102749.109
    ...two -scale screens (Lehner et al. 2006a; Byrne et al. 2007). Inferring functional associations from phylogenetic profiles and genomic context To discover functional associations between genes on the basis of the genomic context of orthologs of C. elegans genes, we used the phylogenetic profile method...
  7. Research: Genic and nongenic contributions to natural variation of quantitative traits in maize
    • Xianran Li,
    • Chengsong Zhu,
    • Cheng-Ting Yeh,
    • Wei Wu,
    • Elizabeth M. Takacs,
    • Katherine A. Petsch,
    • Feng Tian,
    • Guihua Bai,
    • Edward S. Buckler,
    • Gary J. Muehlbauer,
    • Marja C.P. Timmermans,
    • Michael J. Scanlon,
    • Patrick S. Schnable,
    • and Jianming Yu
    Genome Res. December 2012 22: 2436-2444; Published in Advance June 14, 2012, doi:10.1101/gr.140277.112
    ...of TASs located in genic and nongenic regions would shed light on the relative contributions of these two regions to quantitative trait variation. To test whether the final identification of TASs was context independent (i.e., to confirm that identifying a nongenic TAS was not simply due to a high...
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  8. Review: Population genetic inference from genomic sequence variation
    • John E. Pool,
    • Ines Hellmann,
    • Jeffrey D. Jensen,
    • and Rasmus Nielsen
    Genome Res. March 2010 20: 291-300; Published in Advance January 12, 2010, doi:10.1101/gr.079509.108
    ...model or an explicit demographic context. Methods that do infer population parameters from large data sets often focus on the AFS or the genomic means of summary statistics and their variances across the . However, many uniquely informative aspects of wide data—such as long-range haplotype patterns...
  9. ARTICLE: Genome Alignment, Evolution of Prokaryotic Genome Organization, and Prediction of Gene Function Using Genomic Context
    • Yuri I. Wolf,
    • Igor B. Rogozin,
    • Alexey S. Kondrashov,
    • and Eugene V. Koonin
    Genome Res. March 1, 2001 11: 356-372; Published in Advance February 8, 2001, doi:10.1101/gr.161901
    ...Genome Alignment, Evolution of Prokaryotic Genome Organization, and Prediction of Gene Function Using Genomic Context Yuri I. Wolf , Igor B. Rogozin , Alexey S. Kondrashov , and Eugene V. Koonin 1 National Center for Biotechnology...
  10. LETTER: Comparative Analysis of Apicomplexa and Genomic Diversity in Eukaryotes
    • Thomas J. Templeton,
    • Lakshminarayan M. Iyer,
    • Vivek Anantharaman,
    • Shinichiro Enomoto,
    • Juan E. Abrahante,
    • G.M. Subramanian,
    • Stephen L. Hoffman,
    • Mitchell S. Abrahamsen,
    • and L. Aravind
    Genome Res. September 2004 14: 1686-1695; doi:10.1101/gr.2615304
    .... These differences involve both differential gene loss as well as lineage-specific innovations and are discussed further below in the context of specific functional systems. In general, proteins related to functional categories, such as chromatin structure and RNA processing, show fewer domains per protein...
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