Peng, Jian

Widespread macromolecular interaction perturbations in human genetic disorders

Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell

Genomics and Rare DiseasesBioinformatics and Genomic Networks

Cited by 594

A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach

Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., & Li, Z. (2018) · Remote Sensing of Environment

Remote Sensing in AgricultureSmart Agriculture and AI

Cited by 540

High-resolution de novo structure prediction from primary sequence

Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 392

Compact integration of multi-network topology for functional analysis of genes

Cho, H., Berger, B., & Peng, J. (2016) · Cell Systems

Bioinformatics and Genomic NetworksGene Regulatory Network Analysis

Cited by 241

emrQA: A Large Corpus for Question Answering on Electronic Medical Records

Pampari, A., Raghavan, P., Liang, J., & Peng, J. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 191

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Ma, J., Fong, S. H., Luo, Y., Bakkenist, C. J., Shen, J. P., Mourragui, S., Wessels, L. F. A., Hafner, M., Sharan, R., Peng, J., et al. (2021) · Nature Cancer

Computational Drug Discovery MethodsDomain Adaptation and Few-Shot Learning

Cited by 180

Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation

Zhou, J., Ma, J., Chen, Y., Cheng, C., Bao, B., Peng, J., Sejnowski, T. J., Dixon, J. R., & Ecker, J. R. (2019) · Proceedings of the National Academy of Sciences

Genomics and Chromatin DynamicsSingle-cell and spatial transcriptomics

Cited by 180

A marine microbiome antifungal targets urgent-threat drug-resistant fungi

Zhang, F., Zhao, M., Braun, D. R., Ericksen, S. S., Piotrowski, J. S., Nelson, J., Peng, J., Ananiev, G. E., Chanana, S., Barns, K., et al. (2020) · Science

Microbial Natural Products and BiosynthesisMarine Sponges and Natural Products

Cited by 177

Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization

Shan, S., Luo, S., Yang, Z., Hong, J., Su, Y., Ding, F., Fu, L., Li, C., Chen, P., Ma, J., et al. (2022) · Proceedings of the National Academy of Sciences

SARS-CoV-2 and COVID-19 Researchvaccines and immunoinformatics approaches

Cited by 157

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Liu, X., Luo, Y., Li, P., Song, S., & Peng, J. (2021) · PLOS Computational Biology

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 140

Anchor Box Optimization for Object Detection

Zhong, Y., Wang, J., Peng, J., & Zhang, L. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 118

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 114

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Liu, L., Ren, X., Shang, J., Gu, X., Peng, J., & Han, J. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingAdvanced Neural Network Applications

Cited by 45

Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States

Jiang, C., Guan, K., Khanna, M., Chen, L., & Peng, J. (2021) · Environmental Science & Technology

Bioenergy crop production and managementLand Use and Ecosystem Services

Cited by 43

Atom-level generative foundation model for molecular interaction with pockets

Peng, X., Guo, F., Guo, R., Sun, J., Guan, J., Jia, Y., Xu, Y., Huang, Y., Zhang, M., Peng, J., et al. (2024) · Cell

Computational Drug Discovery MethodsProtein Structure and Dynamics

Cited by 22

Unified modeling of 3D molecular generation via atomic interactions with PocketXMol

Peng, X., Guo, R., Guo, F., Wang, Z., Sun, J., Guan, J., Jia, Y., Xu, Y., Huang, Y., Zhang, M., et al. (2026) · Cell

Computational Drug Discovery MethodsProtein Structure and Dynamics

Cited by 22

FlowDesign: Improved Design of Antibody CDRs Through Flow Matching and Better Prior Distributions

Wu, J., Kong, X., Sun, N., Wei, J., Shan, S., Feng, F., Wu, F., Peng, J., Zhang, L., Liu, Y., et al. (2024) · Cell Systems

Monoclonal and Polyclonal Antibodies ResearchProtein Structure and Dynamics

Cited by 11

FlowDesign: Improved design of antibody CDRs through flow matching and better prior distributions

Wu, J., Kong, X., Sun, N., Wei, J., Shan, S., Feng, F., Wu, F., Peng, J., Zhang, L., Liu, Y., et al. (2025) · Cell Systems

Monoclonal and Polyclonal Antibodies ResearchProtein Structure and Dynamics

Cited by 11

Integrating Protein-Protein Interaction Information into Drug Response Prediction by Graph Neural Encoding

Xie, Y., Peng, J., & Zhou, Y. (2019)

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 5