List of Faculty Publications

Below is a list of Faculty publications imported from PubMed or manually added. By default, publications are sorted by year with titles displayed in ascending alphabetical order.
Shortcuts: Wühr, Martin | Wingreen, Ned | Wieschaus, Eric | Troyanskaya, Olga | Tilghman, Shirley | Storey, John | Singh, Mona | Shvartsman, Stanislav | Shaevitz, Joshua | Rabinowitz, Joshua | Murphy, Coleen | Levine, Michael {Levine, Michael S.} | Gregor, Thomas | Botstein, David | Bialek, William | Ayroles, Julien | Andolfatto, Peter | Akey, Joshua

Filters: Author is Zhou, Jian  [Clear All Filters]
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Transcriptome
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Transcription, Genetic
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Transcription Factors
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Systems Biology
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Stem Cells
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
Spatio-Temporal Analysis
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Software
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Signal Transduction
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
Sequence Analysis, DNA
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
RNA, Untranslated
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
RNA Processing, Post-Transcriptional
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Reproducibility of Results
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Quantitative Trait Loci
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
Promoter Regions, Genetic
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
Programming Languages
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Probability
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Polymorphism, Single Nucleotide
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
Phenotype
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Normal Distribution
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Neural Networks (Computer)
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Mutation
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
Mutagenesis
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Models, Statistical
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Models, Genetic
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
Mice
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
Machine Learning
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Kidney
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
Humans
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Histones
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Genomics
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Genome-Wide Association Study
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Genome, Human
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Genetic Predisposition to Disease
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Genetic Association Studies
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Genes, Reporter
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Genes, Developmental
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Gene Library
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Gene Expression Regulation, Developmental
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Gene Expression Regulation
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Gene Expression Profiling
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Gene Expression
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Forecasting
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Fetus
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
Entropy
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Embryonic Development
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Drosophila melanogaster
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Drosophila
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Deep Learning
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Computer Simulation
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Computational Biology
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Chromatin
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Cell Lineage
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
Autism Spectrum Disorder
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Area Under Curve
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
Animals
R. Menon, Otto, E. A., Kokoruda, A., Zhou, J., Zhang, Z., Yoon, E., Chen, Y. - C., Troyanskaya, O., Spence, J. R., Kretzler, M., and Cebrián, C., “Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney.”, Development, vol. 145, no. 16, 2018.
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
Alzheimer Disease
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
Alleles
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.
Algorithms
J. Zhou, Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G., “Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.”, Nat Genet, vol. 50, no. 8, pp. 1171-1179, 2018.
K. M. Chen, Cofer, E. M., Zhou, J., and Troyanskaya, O. G., “Selene: a PyTorch-based deep learning library for sequence data.”, Nat Methods, vol. 16, no. 4, pp. 315-318, 2019.
J. Zhou and Troyanskaya, O. G., “Global quantitative modeling of chromatin factor interactions.”, PLoS Comput Biol, vol. 10, no. 3, p. e1003525, 2014.
J. Zhou, Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., and Troyanskaya, O. G., “Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.”, PLoS Genet, vol. 15, no. 9, p. e1008382, 2019.
J. Zhou, Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., and Troyanskaya, O. G., “Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.”, Nat Genet, vol. 51, no. 6, pp. 973-980, 2019.