Robin Pearce

Molecular Biology · 2019–2022

A researcher in the Science corpus: 8 papers; trusts 141 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Molecular Biology — 350 citations into it; #11,641 of 17,773 by trust here, #8,791 by citations
• Computational Biology & Bioinformatics — 189 citations into it
• Materials Chemistry — 181 citations into it; #7,557 of 11,513 by trust here, #7,964 by citations
• Spectroscopy — 26 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Genetics — 16 citations into it
• Model Architectures — 16 citations into it
• Artificial Intelligence — 12 citations into it

Whom Robin Pearce trusts overall

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

1. Yang Zhang
2. Sitao Wu
3. Johannes Söding
4. Jeffrey Skolnick
5. Yang Li
6. Dong Xu
7. Chengxin Zhang
8. Jianyi Yang
9. David T. Jones
10. Wei Zheng

Who trusts Robin Pearce overall

1. Yang Zhang
2. Tadeo E. Saldaño
3. Xiaogen Zhou
4. Wei Zheng
5. Yang Li

Papers from Robin Pearce's perspective

Ordered by how strongly the researchers Robin Pearce 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. Attention is All you Need (Neural Information Processing Systems)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. TM-align: a protein structure alignment algorithm based on the TM-score
6. Long Short-Term Memory
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
9. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
10. Gradient-based learning applied to document recognition

Their papers

• Deep-learning contact-map guided protein structure prediction in CASP13 (2019) — 182 citations
• Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021) — 502 citations
• Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021) — 112 citations
• Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019) — 47 citations
• De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning (2022) — 82 citations
• I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022) — 417 citations
• Toward the solution of the protein structure prediction problem (2021) — 74 citations
• EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019) — 81 citations