Henrik Nielsen

Molecular Biology · Nature Biotechnology · 2000–2022

A researcher in the Science corpus: 16 papers; trusts 64 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 190 citations into it; #1,124 of 17,773 by trust here, #1,312 by citations
• Computational Biology & Bioinformatics — 61 citations into it; #437 of 660 by trust here, #496 by citations
• Artificial Intelligence — 52 citations into it
• Model Architectures — 31 citations into it; #1,652 of 2,278 by trust here, #1,882 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Ecology — 21 citations into it
• Genetics — 21 citations into it
• Spectroscopy — 19 citations into it

Whom Henrik Nielsen trusts overall

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

1. Anders Krogh
2. Lukas Käll
3. G. von Heijne
4. Agnieszka Sierakowska Juncker
5. Alex Graves
6. Geoffrey E. Hinton
7. Amos Bairoch
8. Søren Brunak
9. Ivan A. Adzhubei
10. Ilya Sutskever

Who trusts Henrik Nielsen overall

1. Kuo-Chen Chou
2. Hongbin Shen
3. Alex L. Mitchell
4. Rajesh Nair
5. Chin-Sheng Yu
6. Michael Heinzinger
7. Burkhard Rost
8. Manoj Bhasin
9. Sujun Hua
10. Ahmed Elnaggar

Papers from Henrik Nielsen's perspective

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

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

Their papers

• Assessing the accuracy of prediction algorithms for classification: an overview (2000) — 2,199 citations
• Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. (2000) — 4,387 citations
• Predicting Secretory Proteins with SignalP. (2017) — 820 citations
• Locating proteins in the cell using TargetP, SignalP and related tools (2007) — 1,142 citations
• SignalP 5.0 improves signal peptide predictions using deep neural networks (2019) — 3,628 citations
• DeepLoc: prediction of protein subcellular localization using deep learning (2017) — 173 citations
• Prediction of human protein function from post-translational modifications and localization features. (2002) — 332 citations
• SignalP 4.0: discriminating signal peptides from transmembrane regions (2011) — 8,860 citations
• NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018) — 550 citations
• Convolutional LSTM Networks for Subcellular Localization of Proteins (2015) — 158 citations
• Prediction of lipoprotein signal peptides in Gram‐negative bacteria (2003) — 1,124 citations
• An introduction to deep learning on biological sequence data: examples and solutions (2017) — 141 citations
• Improved metagenome binning and assembly using deep variational autoencoders (2021) — 522 citations
• SignalP 6.0 predicts all five types of signal peptides using protein language models (2022) — 2,313 citations
• Detecting sequence signals in targeting peptides using deep learning (2019) — 838 citations
• improved prediction of protein structural features by integrated deep learning