Dong-Jun Yu

Molecular Biology · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 97 citations into it; #4,012 of 17,773 by trust here, #9,476 by citations
• Computational Biology & Bioinformatics — 61 citations into it
• Materials Chemistry — 36 citations into it; #8,826 of 11,513 by trust here, #10,845 by citations
• Artificial Intelligence — 15 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 7 citations into it

Whom Dong-Jun Yu trusts overall

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

1. David T. Jones
2. Yang Zhang
3. Magnus Ekeberg
4. Daniel W. A. Buchan
5. Chengxin Zhang
6. Diederik P. Kingma
7. Sitao Wu

Who trusts Dong-Jun Yu overall

1. Robin Pearce
2. Wei Zheng
3. Yang Li
4. Yang Zhang
5. Chengxin Zhang
6. Xiaogen Zhou

Papers from Dong-Jun Yu's perspective

Ordered by how strongly the researchers Dong-Jun Yu 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. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
4. Direct-coupling analysis of residue coevolution captures native contacts across many protein families
5. Attention is All you Need (Neural Information Processing Systems)
6. Correlated mutations and residue contacts in proteins
7. CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations
8. ImageNet classification with deep convolutional neural networks
9. BMC Bioinformatics
10. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Their papers

• 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
• Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks (2020) — 72 citations
• PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations (2022) — 36 citations