Patrick Cramer
Molecular Biology · 2001–2022
A researcher in the Science corpus: 18 papers; trusts 14 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 216 citations into it; #2,352 of 17,773 by trust here, #2,724 by citations
- Structural Biology — 46 citations into it; #72 of 224 by trust here, #96 by citations
- Computational Biology & Bioinformatics — 37 citations into it; #369 of 660 by trust here, #462 by citations
- Surfaces, Coatings and Films — 31 citations into it; #237 of 752 by trust here, #348 by citations
- Materials Chemistry — 23 citations into it; #3,406 of 11,513 by trust here, #10,400 by citations
- Radiation — 10 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Medical Imaging — 7 citations into it
Whom Patrick Cramer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jorge Nocedal
- Sjors H. W. Scheres
- Yann LeCun
- David N. Mastronarde
- S. Buratowski
- A. Shilatifard
- Dirk Schübeler
- Mohammed Alquraishi
- D. Bentley
- L. Eon Bottou
Who trusts Patrick Cramer overall
- Björn Wallner
- Mary C. Thomas
- R. J. Reece
- C. Chiang
- S. Hahn
- Hemali P. Phatnani
- A. Walls
- Damiano Piovesan
- J. Szostak
- Yogesh Kumar
Papers from Patrick Cramer's perspective
Ordered by how strongly the researchers Patrick Cramer trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
Their papers
- Structural Basis of Transcription: RNA Polymerase II at 2.8 Ångstrom Resolution (2001) — 1,249 citations
- RNA velocity of single cells (2018) — 3,593 citations
- AlphaFold2 and the future of structural biology (2021) — 312 citations
- Real-time cryo–EM data pre-processing with Warp (2018) — 884 citations
- Recognition of RNA polymerase II carboxy-terminal domain by 3′-RNA-processing factors (2004) — 343 citations
- Structural basis of RNA polymerase II backtracking, arrest and reactivation (2011) — 361 citations
- Structures of RNA polymerase II complexes with Bye1, a chromatin-binding PHF3/DIDO homologue (2013) — 43 citations
- The interaction landscape between transcription factors and the nucleosome (2017) — 315 citations
- Structural Basis of Transcription: An RNA Polymerase II Elongation Complex at 3.3 Å Resolution (2001) — 908 citations
- Organization and regulation of gene transcription (2019) — 595 citations
- Histone H1 binding to nucleosome arrays depends on linker DNA length and trajectory (2022) — 70 citations
- Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cells (2021) — 435 citations
- Mechanism of molnupiravir-induced SARS-CoV-2 mutagenesis (2021) — 646 citations
- Ruler elements in chromatin remodelers set nucleosome array spacing and phasing (2020) — 62 citations
- Structure of replicating SARS-CoV-2 polymerase (2020) — 741 citations
- Nucleosome-bound SOX2 and SOX11 structures elucidate pioneer factor function (2020) — 232 citations
- TT-seq maps the human transient transcriptome (2016) — 507 citations
- Structural basis of transcription: α-Amanitin–RNA polymerase II cocrystal at 2.8 Å resolution (2002) — 297 citations