Philip M. Kim

Molecular Biology · 2006–2021

A researcher in the Science corpus: 11 papers; trusts 16 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 323 citations into it; #7,666 of 17,773 by trust here, #2,484 by citations
• Materials Chemistry — 89 citations into it
• Computational Biology & Bioinformatics — 68 citations into it
• Spectroscopy — 30 citations into it; #945 of 1,674 by trust here, #1,607 by citations
• Generative Models — 28 citations into it
• Statistical and Nonlinear Physics — 13 citations into it
• Computer Networks and Communications — 7 citations into it

Whom Philip M. Kim trusts overall

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

1. Mark Newman
2. L. Kraal
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Michelle Girvan
6. Colin A. Smith
7. Mark G. F. Sun
8. Albert-László Barabási
9. Yoshua Bengio
10. Gary D. Bader

Who trusts Philip M. Kim overall

1. Elena Zotenko
2. Xiujuan Wang
3. Alexey Strokach
4. Marc F. Lensink
5. Peter Csermely
6. Julián Mestre
7. Ferenc Jorda¤n

Papers from Philip M. Kim's perspective

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

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

Their papers

• Fast and Flexible Protein Design Using Deep Graph Neural Networks. (2020) — 242 citations
• Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing. (2010) — 132 citations
• Classification of Intrinsically Disordered Regions and Proteins (2014) — 1,960 citations
• Relating Three-Dimensional Structures to Protein Networks Provides Evolutionary Insights (2006) — 483 citations
• The Genetic Landscape of a Cell (2010) — 2,188 citations
• The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics (2007) — 1,031 citations
• The Evolutionary Landscape of Alternative Splicing in Vertebrate Species (2012) — 808 citations
• Fast and flexible design of novel proteins using graph neural networks (2019) — 17 citations
• Data driven flexible backbone protein design (2017) — 14 citations
• Protein engineering by highly parallel screening of computationally designed variants (2016) — 42 citations
• Deep Generative Modeling for Protein Design (2021) — 129 citations