Alexandrov, Ludmil B.
University of California San Diego · Moores Cancer Center
31 papers on CoChatORCID 0000-0003-3596-4515
Most cited papers
- Verified
Timing the Initiation of Multiple Myeloma
Rustad, E. H., Yellapantula, V., Leongamornlert, D., Bolli, N., Ledergor, G., Nadeu, F., Angelopoulos, N., Dawson, K. J., Mitchell, T. J., Osborne, R. J., et al. (2020) · Nature Communications
Multiple Myeloma Research and TreatmentsCancer Genomics and Diagnostics
Cited by 165
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Mutational signature analysis identifies MUTYH deficiency in colorectal cancers and adrenocortical carcinomas
Pilati, C., Shinde, J., Alexandrov, L. B., Assié, G., André, T., Hélias‐Rodzewicz, Z., Ducoudray, R., Le Corre, D., Zucman‐Rossi, J., Emile, J., et al. (2017) · The Journal of Pathology
Genetic factors in colorectal cancerCancer Genomics and Diagnostics
Cited by 150
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Nonnegative/Binary matrix factorization with a D-Wave quantum annealer
O’Malley, D., Vesselinov, V. V., Alexandrov, B. S., & Alexandrov, L. B. (2018) · PLOS ONE
Quantum Computing Algorithms and ArchitectureFace and Expression Recognition
Cited by 140
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Germline MC1R status influences somatic mutation burden in melanoma
Robles-Espinoza, C. D., Roberts, N. D., Chen, S., Leacy, F. P., Alexandrov, L. B., Pornputtapong, N., Halaban, R., Krauthammer, M., Cui, R., Timothy Bishop, D., et al. (2016) · Nature Communications
Cutaneous Melanoma Detection and ManagementMelanoma and MAPK Pathways
Cited by 124
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Understanding mutagenesis through delineation of mutational signatures in human cancer
Petljak, M., & Alexandrov, L. B. (2016) · Carcinogenesis
Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer
Cited by 110
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A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers
Glodzik, D., Morganella, S., Davies, H., Simpson, P. T., Li, Y., Zou, X., Diez-Perez, J., Staaf, J., Alexandrov, L. B., Smid, M., et al. (2017) · Nature Genetics
Cancer Genomics and DiagnosticsBRCA gene mutations in cancer
Cited by 84
- Verified
Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides
Bergstrom, E. N., Abbasi, A., Díaz-Gay, M., Galland, L., Ladoire, S., Lippman, S. M., & Alexandrov, L. B. (2024) · Journal of Clinical Oncology
AI in cancer detectionRadiomics and Machine Learning in Medical Imaging
Cited by 83
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