Wei Zheng

Molecular Biology · with Zhang · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 46 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 239 citations into it; #4,159 of 17,773 by trust here, #9,030 by citations
• Computational Biology & Bioinformatics — 147 citations into it
• Materials Chemistry — 124 citations into it; #5,082 of 11,513 by trust here, #8,352 by citations
• Spectroscopy — 19 citations into it
• Model Architectures — 17 citations into it
• Artificial Intelligence — 14 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Optimization & Training — 9 citations into it

Whom Wei Zheng trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yang Zhang
2. Sitao Wu
3. Jeffrey Skolnick
4. Johannes Söding
5. Jianyi Yang
6. Yang Li
7. Chengxin Zhang
8. David T. Jones
9. Kaiming He
10. Diederik P. Kingma

Who trusts Wei Zheng overall

1. Robin Pearce
2. Yang Zhang
3. Yang Li
4. Xiaogen Zhou
5. Chengxin Zhang
6. Jianyi Yang
7. Eric W. Bell
8. Fusong Ju

Papers from Wei Zheng's perspective

Ordered by how strongly the researchers Wei Zheng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Gradient-based learning applied to document recognition
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Deep-learning contact-map guided protein structure prediction in CASP13 (2019) — 182 citations
• DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins (2019) — 159 citations
• Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks (2020) — 72 citations
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021) — 502 citations
• Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019) — 47 citations
• I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022) — 417 citations