Alipanahi, Babak

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology

Genomics and Chromatin DynamicsMachine Learning in Bioinformatics

Cited by 2,873

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology

Parkinson's Disease Mechanisms and TreatmentsGenetic Associations and Epidemiology

Cited by 2,454

Widespread intron retention in mammals functionally tunes transcriptomes

Braunschweig, U., Barbosa-Morais, N. L., Pan, Q., Nachman, E. N., Alipanahi, B., Gonatopoulos-Pournatzis, T., Frey, B., Irimia, M., & Blencowe, B. J. (2014) · Genome Research

RNA Research and SplicingCancer-related molecular mechanisms research

Cited by 664

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., et al. (2022) · Nature Genetics

Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestock

Cited by 654

Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways

Howard, D. M., Adams, M. J., Shirali, M., Clarke, T.-K., Marioni, R. E., Davies, G., Coleman, J. R. I., Alloza, C., Shen, X., Barbu, M. C., et al. (2018) · Nature Communications

Genetic Associations and EpidemiologyTreatment of Major Depression

Cited by 612

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

Genome-wide characteristics of de novo mutations in autism

Yuen, R. K., Merico, D., Cao, H., Pellecchia, G., Alipanahi, B., Thiruvahindrapuram, B., Tong, X., Sun, Y., Cao, D., Zhang, T., et al. (2016) · npj Genomic Medicine

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 216

Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder

Uddin, M., Tammimies, K., Pellecchia, G., Alipanahi, B., Hu, P., Wang, Z., Pinto, D., Lau, L., Nalpathamkalam, T., Marshall, C. R., et al. (2014) · Nature Genetics

Autism Spectrum Disorder ResearchGenetics and Neurodevelopmental Disorders

Cited by 163

Multiethnic GWAS Reveals Polygenic Architecture of Earlobe Attachment

Shaffer, J. R., Li, J., Lee, M. K., Roosenboom, J., Orlova, E., Adhikari, K., Gallo, C., Poletti, G., Schuler-Faccini, L., Bortolini, M.-C., et al. (2017) · The American Journal of Human Genetics

Genetic Associations and EpidemiologyDermatoglyphics and Human Traits

Cited by 47