Himmelstein, Daniel S.

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 2,310

Understanding multicellular function and disease with human tissue-specific networks

Greene, C. S., Krishnan, A., Wong, A. K., Ricciotti, E., Zelaya, R. A., Himmelstein, D. S., Zhang, R., Hartmann, B. M., Zaslavsky, E., Sealfon, S. C., et al. (2015) · Nature Genetics

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 832

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017) · eLife

Computational Drug Discovery MethodsBiomedical Text Mining and Ontologies

Cited by 666

Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

Himmelstein, D. S., & Baranzini, S. E. (2015) · PLOS Computational Biology

Bioinformatics and Genomic NetworksGenetic Associations and Epidemiology

Cited by 172

Mondo: Unifying diseases for the world, by the world

Vasilevsky, N. A., Matentzoglu, N. A., Toro, S., Flack, J. E., Hegde, H., Unni, D. R., Alyea, G. F., Amberger, J. S., Babb, L., Balhoff, J. P., et al. (2022) · medRxiv

Biomedical Text Mining and OntologiesSemantic Web and Ontologies

Cited by 107