Shimano KA, Grimes AB, Kaicker S, et al. Eltrombopag for Newly Diagnosed Pediatric Immune Thrombocytopenia Requiring Treatment: The PINES Randomized Clinical Trial. JAMA. 2025. doi:10.1001/jama.2025.18168
Publications
Lin YY, Breuer K, Weichenhan D, et al. Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold standard. Nucleic acids research. 2025;53(19). doi:10.1093/nar/gkaf970
Ge F, Wang Y, Agerbo E, et al. Contrasting Risk Profiles for Suicide Attempt and Suicide Using Danish Registers and Genetic Data. JAMA psychiatry. 2025. doi:10.1001/jamapsychiatry.2025.3444
Chaudhary R, Moorhead G, Park R, et al. Long-term Survival and Molecular Biomarker Evaluation of a Phase II Cetuximab and Nivolumab Clinical Trial in Recurrent/Metastatic Head and Neck Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2025. doi:10.1158/1078-0432.CCR-25-2201
Barker KS, Zhang Q, Peters TL, et al. Relative contributions of the ERG11 and MRR1A mutations to fluconazole resistance in Clade III Candidozyma (Candida) auris clinical isolates. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases. 2025. doi:10.1016/j.cmi.2025.10.009
Valencia C, Nathan A, Kang JB, Rumker L, Lee H, Raychaudhuri S. Modeling heterogeneity in single-cell perturbation states enhances detection of response eQTLs. Nature genetics. 2025. doi:10.1038/s41588-025-02344-6
St Laurent JD, Xu GD, Ying AW, et al. Shifted assembly and function of mSWI/SNF family subcomplexes underlie targetable dependencies in dedifferentiated endometrial carcinomas. Nature genetics. 2025. doi:10.1038/s41588-025-02333-9
Hemberg M, Marini F, Ghazanfar S, et al. Insights, opportunities, and challenges provided by large cell atlases. Genome biology. 2025;26(1):358. doi:10.1186/s13059-025-03771-8
Ludin A, Stirtz GL, Tal A, et al. CRATER tumor niches facilitate CD8 T cell engagement and correspond with immunotherapy success. Cell. 2025. doi:10.1016/j.cell.2025.09.021
Colin-Leitzinger C, Mekonnen YA, Narvaez-Bandera I, et al. A machine learning framework for classifying lipids in untargeted metabolomics using mass-to-charge ratios and retention times. Metabolomics : Official journal of the Metabolomic Society. 2025;21(6):151. doi:10.1007/s11306-025-02343-y