Mooney, Sean D.

A large-scale evaluation of computational protein function prediction

Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods

Bioinformatics and Genomic NetworksMachine Learning in Bioinformatics

Cited by 1,028

Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.-J., Mort, M., Cooper, D. N., Sebat, J., Iakoucheva, L. M., et al. (2020) · Nature Communications

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 923

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria

Pejaver, V., Byrne, A. B., Feng, B.-J., Pagel, K. A., Mooney, S. D., Karchin, R., O’Donnell-Luria, A., Harrison, S. M., Tavtigian, S. V., Greenblatt, M. S., et al. (2022) · The American Journal of Human Genetics

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 589

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., Lewis, K. A., Georghiou, G., Nguyen, H. N., Hamid, M. N., et al. (2019) · Genome Biology

Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Cited by 452

Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

Pratap, A., Neto, E. C., Snyder, P., Stepnowsky, C., Elhadad, N., Grant, D., Mohebbi, M. H., Mooney, S., Suver, C., Wilbanks, J., et al. (2020) · npj Digital Medicine

Mobile Health and mHealth ApplicationsEthics in Clinical Research

Cited by 410

Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery

Korngiebel, D. M., & Mooney, S. D. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 259

Proteomic analysis of age-dependent changes in protein solubility identifies genes that modulate lifespan

Reis‐Rodrigues, P., Czerwieniec, G., Peters, T. W., Evani, U. S., Alavez, S., Gaman, E. A., Vantipalli, M., Mooney, S. D., Gibson, B. W., Lithgow, G. J., et al. (2012) · Aging Cell

Genetics, Aging, and Longevity in Model OrganismsAdvanced Proteomics Techniques and Applications

Cited by 174

SILAC Analysis Reveals Increased Secretion of Hemostasis-Related Factors by Senescent Cells

Wiley, C. D., Liu, S., Limbad, C., Zawadzka, A. M., Beck, J., Demaria, M., Artwood, R., Alimirah, F., Lopez-Dominguez, J.-A., Kuehnemann, C., et al. (2019) · Cell Reports

Telomeres, Telomerase, and SenescenceGenetics, Aging, and Longevity in Model Organisms

Cited by 137

The accuracy of passive phone sensors in predicting daily mood

Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., & Areán, P. A. (2019) · Depression and Anxiety

Mental Health Research TopicsDigital Mental Health Interventions

Cited by 119

Calibration of Multiple In Silico Tools for Predicting Pathogenicity of Mismatch Repair Gene Missense Substitutions

Thompson, B. A., Greenblatt, M. S., Vallee, M. P., Herkert, J. C., Tessereau, C., Young, E. L., Adzhubey, I. A., Li, B., Bell, R., Feng, B., et al. (2013) · Human Mutation

Genomics and Rare DiseasesGenetic factors in colorectal cancer

Cited by 95