Bent Petersen
Molecular Biology · with Nielsen · 2009–2022
A researcher in the Science corpus: 4 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 51 citations into it
- Computational Biology & Bioinformatics — 29 citations into it
Whom Bent Petersen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Bent Petersen overall
- Marcin J. Skwark
- Morten Nielsen
- Sebastian A. Wagner
- Michael Schantz Klausen
- Manuel Irimia
- Mirco Michel
- Claus Lundegaard
- Daniele Raimondi
Papers from Bent Petersen's perspective
Ordered by how strongly the researchers Bent Petersen trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Clustering of highly homologous sequences to reduce the size of large protein databases
- Highly accurate protein structure prediction with AlphaFold
- HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- The Protein Data Bank
- Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
- Protein disorder prediction: implications for structural proteomics.
- Capturing non‐local interactions by long short‐term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and…
- BLAST+: architecture and applications
Their papers
- A generic method for assignment of reliability scores applied to solvent accessibility predictions (2009) — 646 citations
- NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018) — 550 citations
- NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning (2022) — 295 citations
- improved prediction of protein structural features by integrated deep learning