McLean, Cory Y.

GREAT improves functional interpretation of cis-regulatory regions

McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology

Genomics and Chromatin DynamicsBioinformatics and Genomic Networks

Cited by 4,502

A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 1,840

Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma

Johnson, B. E., Mazor, T., Hong, C., Barnes, M., Aihara, K., McLean, C. Y., Fouse, S. D., Yamamoto, S., Ueda, H., Tatsuno, K., et al. (2014) · Science

Glioma Diagnosis and TreatmentCancer Genomics and Diagnostics

Cited by 1,362

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 537

Human-specific loss of regulatory DNA and the evolution of human-specific traits

McLean, C. Y., Reno, P. L., Pollen, A. A., Bassan, A. I., Capellini, T. D., Guenther, C., Indjeian, V. B., Lim, X., Menke, D. B., Schaar, B. T., et al. (2011) · Nature

Chromosomal and Genetic VariationsGenomic variations and chromosomal abnormalities

Cited by 535

Quantifying prion disease penetrance using large population control cohorts

Minikel, E. V., Vallabh, S. M., Lek, M., Estrada, K., Samocha, K. E., Sathirapongsasuti, J. F., McLean, C. Y., Tung, J. Y., Yu, L. P. C., Gambetti, P., et al. (2016) · Science Translational Medicine

Prion Diseases and Protein MisfoldingGenomics and Rare Diseases

Cited by 442

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

Nalls, M. A., McLean, C. Y., Rick, J., Eberly, S., Hutten, S. J., Gwinn, K., Sutherland, M., Martinez, M., Heutink, P., Williams, N. M., et al. (2015) · The Lancet Neurology

Parkinson's Disease Mechanisms and TreatmentsDementia and Cognitive Impairment Research

Cited by 204

Improving Phenotypic Measurements in High-Content Imaging Screens

Ando, D. M., McLean, C. Y., & Berndl, M. (2017) · bioRxiv (Cold Spring Harbor Laboratory)

Cell Image Analysis TechniquesComputational Drug Discovery Methods

Cited by 96

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Advancing Multimodal Medical Capabilities of Gemini

Yang, L., Xu, S., Sellergren, A., Kohlberger, T., Zhou, Y., Ktena, I., Kiraly, A., Ahmed, F., Hormozdiari, F., Jaroensri, T., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 34

DeepTrio: Variant Calling in Families Using Deep Learning

Brambrink, L., Kolesnikov, A., Goel, S., Nattestad, M., Yun, T., Baid, G., Yang, H., McLean, C., Shafin, K., Chang, P.-C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 32