Michael Jendrusch

Molecular Biology · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 80 citations into it; #12,089 of 17,773 by trust here, #13,284 by citations
• Computational Biology & Bioinformatics — 41 citations into it
• Materials Chemistry — 26 citations into it; #7,607 of 11,513 by trust here, #9,927 by citations
• Computational Theory and Mathematics — 10 citations into it
• Spectroscopy — 9 citations into it

Whom Michael Jendrusch trusts overall

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

1. S. Parasuraman
2. Surojit Biswas
3. Po-Ssu Huang
4. Yang Zhang
5. David Baker
6. W. Sheffler
7. Daniel D. Lee
8. Ivan V. Anishchenko
9. E. C. Alley
10. John Ingraham

Who trusts Michael Jendrusch overall

1. Kevin Kaichuang Yang
2. Casper A. Goverde
3. Ivan V. Anishchenko
4. Niccoló Zanichelli
5. Benedict Wolf

Papers from Michael Jendrusch's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. ImageNet classification with deep convolutional neural networks
7. Improved protein structure prediction using predicted interresidue orientations
8. Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules
9. GENERATIVE ADVERSARIAL NETS
10. Gradient-based learning applied to document recognition

Their papers

• AlphaDesign: A de novo protein design framework based on AlphaFold (2021) — 79 citations
• Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins (2019) — 28 citations