Qi, Yanjun

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

Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Gao, J., Lanchantin, J., Soffa, M. L., & Qi, Y. (2018) · 2018 IEEE Security and Privacy Workshops (SPW)

Adversarial Robustness in Machine LearningSoftware Testing and Debugging Techniques

Cited by 693

A critical assessment of Mus musculusgene function prediction using integrated genomic evidence

Peña-Castillo, L., Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. K., et al. (2008) · Genome Biology

Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Cited by 248

Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

Singh, R., Kuscu, C., Quinlan, A., Qi, Y., & Adli, M. (2015) · Nucleic Acids Research

CRISPR and Genetic EngineeringRNA regulation and disease

Cited by 217

Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data

Lanchantin, J., & Qi, Y. (2020) · Bioinformatics

Genomics and Chromatin DynamicsEpigenetics and DNA Methylation

Cited by 17

Measuring Visual Generalization in Continuous Control from Pixels

Grigsby, J., & Qi, Y. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring

Wang, Z., Brand, R., Adolf-Bryfogle, J., Grewal, J., Qi, Y., Combs, S. A., Golovach, N., Alford, R., Rangwala, H., & Clark, P. M. (2024) · ACS Omega

Computational Drug Discovery MethodsProtein Structure and Dynamics

Cited by 3