Jianzhu Ma
Molecular Biology · 2012–2023
A researcher in the Science corpus: 18 papers; trusts 304 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 434 citations into it; #1,054 of 17,773 by trust here, #2,461 by citations
- Materials Chemistry — 168 citations into it; #2,917 of 11,513 by trust here, #8,144 by citations
- Computational Biology & Bioinformatics — 163 citations into it; #152 of 660 by trust here, #259 by citations
- Artificial Intelligence — 122 citations into it
- Computer Vision and Pattern Recognition — 122 citations into it
- Generative Models — 93 citations into it
- Computational Theory and Mathematics — 74 citations into it; #5,332 of 12,182 by trust here, #1,890 by citations
- Natural Language Processing — 37 citations into it
Whom Jianzhu Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yang Zhang
- Johannes Söding
- Jinbo Xu
- Jian Peng
- Sheng Wang
- Sean R. Eddy
- Dong C. Liu
- Feng Zhao
- Geoffrey E. Hinton
- Zhiyong Wang
Who trusts Jianzhu Ma overall
- Sheng Wang
- Jinbo Xu
- Daniel W. A. Buchan
- Jie Hou
- Shiwang Liu
- Siqi Sun
- Chunxiang Peng
- Liisa Holm
- Mu Gao
- Zhiyong Wang
Papers from Jianzhu Ma's perspective
Ordered by how strongly the researchers Jianzhu Ma trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation (2023) — 407 citations
- Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields (2015) — 544 citations
- Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013) — 112 citations
- MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014) — 74 citations
- Protein structure alignment beyond spatial proximity (2013) — 172 citations
- Protein threading using context-specific alignment potential (2013) — 143 citations
- A conditional neural fields model for protein threading (2012) — 87 citations
- DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015) — 67 citations
- Protein Homology Detection Through Alignment of Markov Random Fields (2015) — 14 citations
- AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields (2016) — 121 citations
- Using deep learning to model the hierarchical structure and function of a cell (2018) — 391 citations
- Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets (2022) — 333 citations
- DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields (2017) — 13 citations
- Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022) — 305 citations
- High-resolution de novo structure prediction from primary sequence (2022) — 529 citations
- 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (2023) — 326 citations
- Large Scale Foundation Model on Single-cell Transcriptomics (2023) — 584 citations
- Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020) — 421 citations