Chengxin Zhang

Molecular Biology · 2017–2022

A researcher in the Science corpus: 14 papers; trusts 92 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 403 citations into it; #1,969 of 17,773 by trust here, #4,132 by citations
• Computational Biology & Bioinformatics — 231 citations into it
• Materials Chemistry — 192 citations into it; #3,145 of 11,513 by trust here, #6,682 by citations
• Spectroscopy — 35 citations into it
• Model Architectures — 29 citations into it
• Artificial Intelligence — 27 citations into it
• Computational Theory and Mathematics — 16 citations into it; #11,385 of 12,182 by trust here, #5,954 by citations
• Optimization & Training — 15 citations into it

Whom Chengxin Zhang trusts overall

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

1. Yang Zhang
2. Jeffrey Skolnick
3. Sitao Wu
4. Jianyi Yang
5. David T. Jones
6. Johannes Söding
7. Dong Xu
8. Ambrish Roy
9. Yang Li
10. Kaiming He

Who trusts Chengxin Zhang overall

1. Robin Pearce
2. Yang Zhang
3. Yang Li
4. Wei Zheng
5. Xiaogen Zhou
6. Mohammed Alquraishi
7. Eric W. Bell
8. Mirko Torrisi
9. Rhiju Das
10. Lukás Jendele

Papers from Chengxin Zhang's perspective

Ordered by how strongly the researchers Chengxin Zhang 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. Learning Multiple Layers of Features from Tiny Images

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
• US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes (2022) — 437 citations
• RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA (2019) — 86 citations
• Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017) — 88 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
• COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017) — 528 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 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