Mu Gao
Molecular Biology · 2001–2022
A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 81 citations into it; #14,791 of 17,773 by trust here, #14,509 by citations
- Computational Biology & Bioinformatics — 47 citations into it
- Materials Chemistry — 34 citations into it; #9,322 of 11,513 by trust here, #10,845 by citations
- Computational Theory and Mathematics — 11 citations into it; #11,069 of 12,182 by trust here, #10,349 by citations
- Spectroscopy — 8 citations into it
- Artificial Intelligence — 6 citations into it
- Radiology, Nuclear Medicine and Imaging — 6 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Mu Gao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Adam T. Zemla
- Yang Zhang
- Jeffrey Skolnick
- Patrick Aloy
- Sheng Wang
- Hongyi Zhou
- Angel R. Ortiz
- David T. Jones
- Evan Shelhamer
- Alexandre M. J. J. Bonvin
Who trusts Mu Gao overall
Papers from Mu Gao's perspective
Ordered by how strongly the researchers Mu Gao trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Long Short-Term Memory
- Going deeper with convolutions
- HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
- Learning Multiple Layers of Features from Tiny Images
Their papers
- AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) — 229 citations
- New benchmark metrics for protein-protein docking methods (2011) — 44 citations
- DESTINI: A deep-learning approach to contact-driven protein structure prediction (2019) — 64 citations
- Steered molecular dynamics and mechanical functions of proteins. (2001) — 928 citations