Philippakis, Anthony A.

A structural variation reference for medical and population genetics

Collins, R. L., Brand, H., Karczewski, K. J., Zhao, X., Alföldi, J., Francioli, L. C., Khera, A. V., Lowther, C., Gauthier, L. D., Wang, H., et al. (2020) · Nature

Genomic variations and chromosomal abnormalitiesGenomics and Rare Diseases

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Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender

Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2023) · Nature Methods

Single-cell and spatial transcriptomicsGene expression and cancer classification

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Genomic data in the all of us research program

The All of Us Research Program Genomics Investigators, Manuscript Writing Group, Bick, A. G., Metcalf, G. A., Mayo, K. R., Lichtenstein, L., Rura, S., Carroll, R. J., Musick, A., Linder, J. E., et al. (2024) · Nature

Genetic Associations and EpidemiologyGenomics and Rare Diseases

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Variation in Homeodomain DNA Binding Revealed by High-Resolution Analysis of Sequence Preferences

Berger, M. F., Badis, G., Gehrke, A. R., Talukder, S., Philippakis, A. A., Peña-Castillo, L., Alleyne, T. M., Mnaimneh, S., Botvinnik, O. B., Chan, E. T., et al. (2008) · Cell

Genomics and Chromatin DynamicsProtein Structure and Dynamics

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International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases

Boycott, K. M., Rath, A., Chong, J. X., Hartley, T., Alkuraya, F. S., Baynam, G., Brookes, A. J., Brudno, M., Carracedo, A., den Dunnen, J. T., et al. (2017) · The American Journal of Human Genetics

Genomics and Rare DiseasesConnective tissue disorders research

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Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes

Karczewski, K. J., Solomonson, M., Chao, K. R., Goodrich, J. K., Tiao, G., Lu, W., Riley-Gillis, B. M., Tsai, E. A., Kim, H. I., Zheng, X., et al. (2022) · Cell Genomics

Genetic Associations and EpidemiologyGenomics and Rare Diseases

Cited by 284

Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy

Pirruccello, J. P., Bick, A., Wang, M., Chaffin, M., Friedman, S., Yao, J., Guo, X., Venkatesh, B. A., Taylor, K. D., Post, W. S., et al. (2020) · Nature Communications

Cardiomyopathy and Myosin StudiesGenetic Associations and Epidemiology

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Deep learning enables genetic analysis of the human thoracic aorta

Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., et al. (2022) · Nature Genetics

Genetic Associations and EpidemiologyAortic Disease and Treatment Approaches

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Unsupervised removal of systematic background noise from droplet-based single-cell experiments using <tt>CellBender</tt>

Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory)

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 164

Machine learning enables new insights into genetic contributions to liver fat accumulation

Haas, M. E., Pirruccello, J. P., Friedman, S. N., Wang, M., Emdin, C. A., Ajmera, V. H., Simon, T. G., Homburger, J. R., Guo, X., Budoff, M., et al. (2021) · Cell Genomics

Liver Disease Diagnosis and TreatmentGenetic Associations and Epidemiology

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Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction

The Myocardial Infarction Genetics Consortium, Shea, J., Agarwala, V., Philippakis, A. A., Maguire, J., Banks, E., DePristo, M., Thomson, B., Guiducci, C., Onofrio, R. C., et al. (2011) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

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Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., & Uhler, C. (2023) · Nature Communications

ECG Monitoring and AnalysisMachine Learning in Healthcare

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How will machine learning inform the clinical care of atrial fibrillation?

Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A. (2020) · Circulation Research

Atrial Fibrillation Management and OutcomesMachine Learning in Healthcare

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Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial: A Secondary Analysis of a Randomized Clinical Trial

Cunningham, J. W., Singh, P., Reeder, C., Claggett, B., Marti-Castellote, P. M., Lau, E. S., Khurshid, S., Batra, P., Lubitz, S. A., Maddah, M., et al. (2024) · JAMA Cardiology

Artificial Intelligence in HealthcareHeart Failure Treatment and Management

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Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure

Khurshid, S., Friedman, S. F., Kany, S., D’Souza, V., Roshandelpoor, A., Kosyakovsky, L. B., Chitsazan, M., Cunningham, J. W., Singh, P., Lau, E. S., et al. (2026) · Circulation: Heart Failure

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

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