Charney, Alexander W.

An inflammatory cytokine signature predicts COVID-19 severity and survival

Del Valle, D. M., Kim-Schulze, S., Huang, H.-H., Beckmann, N. D., Nirenberg, S., Wang, B., Lavin, Y., Swartz, T. H., Madduri, D., Stock, A., et al. (2020) · Nature Medicine

COVID-19 Clinical Research StudiesSARS-CoV-2 and COVID-19 Research

Cited by 2,605

Genome-wide association study identifies 30 loci associated with bipolar disorder

eQTLGen Consortium, Stahl, E. A., BIOS Consortium, the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., et al. (2019) · Nature Genetics

Bipolar Disorder and TreatmentGenetic Associations and Epidemiology

Cited by 1,519

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics

Bipolar Disorder and TreatmentGenetic Associations and Epidemiology

Cited by 1,517

Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder

Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science

Autism Spectrum Disorder ResearchSchizophrenia research and treatment

Cited by 1,247

Comprehensive functional genomic resource and integrative model for the human brain

Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 965

Integrative functional genomic analysis of human brain development and neuropsychiatric risks

Li, M., Santpere, G., Imamura Kawasawa, Y., Evgrafov, O. V., Gulden, F. O., Pochareddy, S., Sunkin, S. M., Li, Z., Shin, Y., Zhu, Y., et al. (2018) · Science

Genetics and Neurodevelopmental DisordersGenomics and Chromatin Dynamics

Cited by 810

AKI in Hospitalized Patients with COVID-19

Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., et al. (2021) · Journal of the American Society of Nephrology

Acute Kidney Injury ResearchCOVID-19 Clinical Research Studies

Cited by 805

Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection

Lala, A., Johnson, K. W., Januzzi, J. L., Russak, A. J., Paranjpe, I., Richter, F., Zhao, S., Somani, S., Van Vleck, T., Vaid, A., et al. (2020) · Journal of the American College of Cardiology

COVID-19 Clinical Research StudiesTakotsubo Cardiomyopathy and Associated Phenomena

Cited by 791

Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C)

Gruber, C. N., Patel, R. S., Trachtman, R., Lepow, L., Amanat, F., Krammer, F., Wilson, K. M., Onel, K., Geanon, D., Tuballes, K., et al. (2020) · Cell

Kawasaki Disease and Coronary ComplicationsAutoimmune and Inflammatory Disorders Research

Cited by 551

Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments

Brin, D., Sorin, V., Vaid, A., Soroush, A., Glicksberg, B. S., Charney, A. W., Nadkarni, G., & Klang, E. (2023) · Scientific Reports

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 319

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., Allen, M., Reddy, J. S., Hoffman, G. E., Dang, K. K., Calley, J., Ebert, P. J., Eddy, J., et al. (2020) · Scientific Data

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 318

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., et al. (2020) · JMIR Medical Informatics

Machine Learning in HealthcarePrivacy-Preserving Technologies in Data

Cited by 253

A Role for Noncoding Variation in Schizophrenia

Roussos, P., Mitchell, A. C., Voloudakis, G., Fullard, J. F., Pothula, V. M., Tsang, J., Stahl, E. A., Georgakopoulos, A., Ruderfer, D. M., Charney, A., et al. (2014) · Cell Reports

Genetic Associations and EpidemiologySchizophrenia research and treatment

Cited by 251

Large Language Models and Empathy: Systematic Review

Sorin, V., Brin, D., Barash, Y., Konen, E., Charney, A., Nadkarni, G., & Klang, E. (2024) · Journal of Medical Internet Research

Artificial Intelligence in Healthcare and EducationEmpathy and Medical Education

Cited by 240

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Mullins, N., Kang, J., Campos, A. I., Coleman, J. R. I., Edwards, A. C., Galfalvy, H., Levey, D. F., Lori, A., Shabalin, A., Starnawska, A., et al. (2022) · Biological Psychiatry

Suicide and Self-Harm StudiesGenetic Associations and Epidemiology

Cited by 238

Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support

Omar, M., Sorin, V., Collins, J. D., Reich, D., Freeman, R., Gavin, N., Charney, A., Stump, L., Bragazzi, N. L., Nadkarni, G. N., et al. (2025) · Communications Medicine

Adversarial Robustness in Machine LearningArtificial Intelligence in Healthcare and Education

Cited by 204

Sociodemographic biases in medical decision making by large language models

Omar, M., Soffer, S., Agbareia, R., Bragazzi, N. L., Apakama, D. U., Horowitz, C. R., Charney, A. W., Freeman, R., Kummer, B., Glicksberg, B. S., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 173

Multiscale causal networks identify VGF as a key regulator of Alzheimer’s disease

Beckmann, N. D., Lin, W.-J., Wang, M., Cohain, A. T., Charney, A. W., Wang, P., Ma, W., Wang, Y.-C., Jiang, C., Audrain, M., et al. (2020) · Nature Communications

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 172

Blood–brain-barrier-crossing lipid nanoparticles for mRNA delivery to the central nervous system

Wang, C., Xue, Y., Markovic, T., Li, H., Wang, S., Zhong, Y., Du, S., Zhang, Y., Hou, X., Yu, Y., et al. (2025) · Nature Materials

RNA Interference and Gene DeliveryNanoparticle-Based Drug Delivery

Cited by 171

Genetics of the human microglia regulome refines Alzheimer’s disease risk loci

Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics

Neuroinflammation and Neurodegeneration MechanismsAlzheimer's disease research and treatments

Cited by 168

Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying

Soroush, A., Glicksberg, B. S., Zimlichman, E., Barash, Y., Freeman, R., Charney, A. W., Nadkarni, G. N., & Klang, E. (2024) · NEJM AI

Medical Coding and Health InformationArtificial Intelligence in Healthcare and Education

Cited by 137

Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders

Hu, B., Won, H., Mah, W., Park, R. B., Kassim, B., Spiess, K., Kozlenkov, A., Crowley, C. A., Pochareddy, S., The PsychENCODE Consortium, et al. (2021) · Nature Communications

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 103

Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room

Glicksberg, B. S., Timsina, P., Patel, D., Sawant, A., Vaid, A., Raut, G., Charney, A. W., Apakama, D., Carr, B. G., Freeman, R., et al. (2024) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 82

Extracting social support and social isolation information from clinical psychiatry notes: comparing a rule-based natural language processing system and a large language model

Patra, B. G., Lepow, L. A., Kasi Reddy Jagadeesh Kumar, P., Vekaria, V., Sharma, M. M., Adekkanattu, P., Fennessy, B., Hynes, G., Landi, I., Sanchez-Ruiz, J. A., et al. (2025) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 32