Yang Li

Molecular Biology · with Zhang · 2019–2022

A researcher in the Science corpus: 9 papers; trusts 66 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 307 citations into it; #2,437 of 17,773 by trust here, #6,410 by citations
• Computational Biology & Bioinformatics — 186 citations into it
• Materials Chemistry — 148 citations into it; #5,081 of 11,513 by trust here, #8,144 by citations
• Model Architectures — 28 citations into it
• Artificial Intelligence — 27 citations into it
• Spectroscopy — 24 citations into it
• Optimization & Training — 15 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it

Whom Yang Li trusts overall

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

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

Who trusts Yang Li overall

1. Robin Pearce
2. Yang Zhang
3. Wei Zheng
4. Chengxin Zhang
5. Xiaogen Zhou
6. Shaun M. Kandathil
7. Eric W. Bell
8. Chunxiang Peng
9. Joe G. Greener
10. Jianyi Yang

Papers from Yang Li's perspective

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

Their papers

• Deep-learning contact-map guided protein structure prediction in CASP13 (2019) — 182 citations
• ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks (2019) — 146 citations
• Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 (2019) — 88 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
• Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022) — 100 citations