Chee Keong Kwoh

Molecular Biology · 2009–2021

A researcher in the Science corpus: 9 papers; trusts 25 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 96 citations into it; #7,541 of 17,773 by trust here, #7,911 by citations
• Computational Theory and Mathematics — 47 citations into it; #6,623 of 12,182 by trust here, #2,104 by citations
• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Computational Chemistry & Drug Discovery — 15 citations into it
• Materials Chemistry — 12 citations into it
• Cell Biology — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Chee Keong Kwoh trusts overall

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

1. Gary D. Bader
2. José B. Pereira-Leal
3. Sylvain Brohée
4. Anil K. Jain
5. Diederik P. Kingma
6. T. van Laarhoven
7. Jimmy Ba
8. Masahiro Hattori
9. Risi Kondor
10. Christopher W. V. Hogue

Who trusts Chee Keong Kwoh overall

1. Yuhao Wang
2. Shitong Luo
3. Gabriele Corso
4. Hannes Stärk

Papers from Chee Keong Kwoh's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. ImageNet classification with deep convolutional neural networks
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Long Short-Term Memory
8. Layer Normalization
9. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• Fast, accurate, and reliable molecular docking with QuickVina 2 (2015) — 387 citations
• Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration (2017) — 312 citations
• Drug-target interaction prediction by learning from local information and neighbors (2013) — 347 citations
• CovalentDock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints (2013) — 129 citations
• Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010) — 345 citations
• A core-attachment based method to detect protein complexes in PPI networks (2009) — 351 citations
• An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG (2021) — 535 citations
• Time-Series Representation Learning via Temporal and Contextual Contrasting (2021) — 834 citations
• Ultra-Scalable Spectral Clustering and Ensemble Clustering (2019) — 448 citations