Martin Kircher, Ph.D.
Affiliations: | University of Washington, Seattle, Seattle, WA |
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"Martin Kircher"
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| Schubach M, Maass T, Nazaretyan L, et al. (2024) CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Research. 52: D1143-D1154 |
| Agarwal V, Inoue F, Schubach M, et al. (2023) Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types. Biorxiv : the Preprint Server For Biology |
| Mensah MA, Niskanen H, Magalhaes AP, et al. (2023) Aberrant phase separation and nucleolar dysfunction in rare genetic diseases. Nature |
| Whalen S, Inoue F, Ryu H, et al. (2023) Machine learning dissection of human accelerated regions in primate neurodevelopment. Neuron |
| Schubach M, Nazaretyan L, Kircher M. (2022) The Regulatory Mendelian Mutation score for GRCh38. Gigascience. 12 |
| Kircher M, Ludwig KU. (2022) Systematic assays and resources for the functional annotation of non-coding variants. Medizinische Genetik : Mitteilungsblatt Des Berufsverbandes Medizinische Genetik E.V. 34: 275-286 |
| Johnsen JM, Fletcher SN, Dove A, et al. (2022) Results of Genetic Analysis of 11,341 Participants Enrolled in the My Life, Our Future Hemophilia Genotyping Initiative in the United States. Journal of Thrombosis and Haemostasis : Jth |
| Spielmann M, Kircher M. (2022) Computational and experimental methods for classifying variants of unknown clinical significance. Cold Spring Harbor Molecular Case Studies. 8 |
| Kleinert P, Kircher M. (2022) A framework to score the effects of structural variants in health and disease. Genome Research |
| Rentzsch P, Schubach M, Shendure J, et al. (2021) CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Medicine. 13: 31 |