Dunlap G, Wagner A, Meednu N, et al. Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.03.18.533282
Publications
Zhou H, Kember RL, Deak JD, et al. Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.01.24.23284960
Akshay A, Katoch M, Abedi M, et al. SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.06.28.533479
Hu Y, Ma S, Kartha VK, et al. Single-cell multi-scale footprinting reveals the modular organization of DNA regulatory elements. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.03.28.533945
Edelstein GE, Boucau J, Uddin R, et al. SARS-CoV-2 virologic rebound with nirmatrelvir-ritonavir therapy. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.06.23.23288598
Chen S, Neale BM, Berkovic SF, . Shared and distinct ultra-rare genetic risk for diverse epilepsies: A whole-exome sequencing study of 54,423 individuals across multiple genetic ancestries. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.02.22.23286310
Miller-Fleming TW, Allos A, Gantz E, et al. Developing a Phenotype Risk Score for Tic Disorders in a Large, Clinical Biobank. medRxiv : the preprint server for health sciences. 2023. doi:10.1101/2023.02.21.23286253
Gibson WJ, Sadagopan A, Shoba VM, Choudhary A, Meyerson M, Schreiber SL. Bifunctional small molecules that induce nuclear localization and targeted transcriptional regulation. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.07.07.548101
Yurkovetskiy L, Egri S, Kurhade C, et al. S:D614G and S:H655Y are gateway mutations that act epistatically to promote SARS-CoV-2 variant fitness. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.03.30.535005
Akshay A, Katoch M, Shekarchizadeh N, et al. Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis. bioRxiv : the preprint server for biology. 2023. doi:10.1101/2023.07.04.546825