Po-Ssu Huang

Molecular Biology · 2007–2022

A researcher in the Science corpus: 20 papers; trusts 117 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 652 citations into it; #1,426 of 17,773 by trust here, #1,066 by citations
• Materials Chemistry — 310 citations into it; #4,428 of 11,513 by trust here, #3,987 by citations
• Computational Biology & Bioinformatics — 184 citations into it; #102 of 660 by trust here, #143 by citations
• Computational Chemistry & Drug Discovery — 75 citations into it
• Computational Theory and Mathematics — 75 citations into it
• Radiology, Nuclear Medicine and Imaging — 73 citations into it; #778 of 3,265 by trust here, #396 by citations
• Spectroscopy — 71 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it

Whom Po-Ssu Huang trusts overall

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

1. David Baker
2. Roland L. Dunbrack
3. Adrian A. Canutescu
4. Alec Radford
5. Stephen L. Mayo
6. Rhiju Das
7. Individualized Treat
8. Daniel J. Mandell
9. James J. Havranek
10. Mohammed Alquraishi

Who trusts Po-Ssu Huang overall

1. Namrata Anand
2. Raphael R. Eguchi
3. I. Korendovych
4. I. Samish
5. John Ingraham
6. Xingjie Pan
7. Michael Jendrusch
8. Namrata Anand-Achim
9. Robin Pearce
10. Kevin Kaichuang Yang

Papers from Po-Ssu Huang's perspective

Ordered by how strongly the researchers Po-Ssu Huang trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Protein Structure Determination using Metagenome sequence data (2017) — 478 citations
• The coming of age of de novo protein design (2016) — 1,345 citations
• RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design (2011) — 376 citations
• Protein sequence design with a learned potential (2020) — 135 citations
• Accurate de novo design of hyperstable constrained peptides (2016) — 389 citations
• Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
• De novo design of a fluorescence-activating β-barrel (2018) — 312 citations
• ProGen: Language Modeling for Protein Generation (2020) — 353 citations
• Structure-based Protein Design with Deep Learning (2021) — 95 citations
• A de novo designed protein–protein interface (2007) — 102 citations
• Corrigendum: Design of a hyperstable 60-subunit protein icosahedron (2016) — 45 citations
• Design of a hyperstable 60-subunit protein icosahedron (2016) — 410 citations
• A potent and broad neutralizing antibody recognizes and penetrates the HIV glycan shield (2011) — 731 citations
• Computational Design of Transmembrane Pores (2020) — 141 citations
• Rational HIV immunogen design to target specific germline B cell receptors (2013) — 697 citations
• Protein sequence design with a learned potential (2022) — 69 citations
• Tight and specific lanthanide binding in a de novo TIM barrel with a large internal cavity designed by symmetric domain fusion (2020) — 65 citations
• Fully differentiable full-atom protein backbone generation (2019) — 55 citations
• Generative modeling for protein structures (2018) — 176 citations
• De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy (2015) — 245 citations