Sergei Grudinin

Molecular Biology · 2016–2021

A researcher in the Science corpus: 8 papers; trusts 47 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 166 citations into it; #4,139 of 17,773 by trust here, #5,550 by citations
• Materials Chemistry — 104 citations into it; #2,401 of 11,513 by trust here, #5,047 by citations
• Computational Biology & Bioinformatics — 74 citations into it
• Computational Theory and Mathematics — 45 citations into it; #6,708 of 12,182 by trust here, #2,015 by citations
• Spectroscopy — 23 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Artificial Intelligence — 14 citations into it

Whom Sergei Grudinin trusts overall

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

1. Kliment Olechnovic
2. Adam T. Zemla
3. Thorsten Joachims
4. Geoffrey E. Hinton
5. John Moult
6. Diederik P. Kingma
7. Andriy Kryshtafovych
8. Yang Zhang
9. Arjun Ray
10. Sergey Ioffe

Who trusts Sergei Grudinin overall

1. Bowen Jing
2. Raphael J. L. Townshend
3. Xiao Wang
4. Stephan Eismann
5. Patricia Suriana
6. Guillaume Pagès
7. Vignesh Ram Somnath
8. Soumya Sanyal
9. Yumeng Yan

Papers from Sergei Grudinin's perspective

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

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

Their papers

• Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment (2016) — 130 citations
• Smooth orientation-dependent scoring function for coarse-grained protein quality assessment (2018) — 52 citations
• Deep convolutional networks for quality assessment of protein folds (2018) — 119 citations
• Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019) — 104 citations
• Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment (2021) — 103 citations
• Protein model quality assessment using 3D oriented convolutional neural networks (2018) — 82 citations
• Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry (2016) — 48 citations
• Assessment of chemical‐crosslink‐assisted protein structure modeling in CASP13 (2020) — 20 citations