Fangping Wan

Molecular Biology · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 50 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 66 citations into it; #3,857 of 17,773 by trust here, #11,608 by citations
• Computational Theory and Mathematics — 61 citations into it; #3,587 of 12,182 by trust here, #4,531 by citations
• Computational Chemistry & Drug Discovery — 27 citations into it
• Materials Chemistry — 24 citations into it
• Artificial Intelligence — 22 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Computational Biology & Bioinformatics — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Fangping Wan trusts overall

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

1. Geoffrey E. Hinton
2. Tomas Mikolov
3. Ilya Sutskever
4. Yoshua Bengio
5. The Uniprot Consortium
6. S. Parasuraman
7. Sergey Ioffe
8. Yoshihiro Yamanishi
9. Alex Krizhevsky
10. L. V. D. van der Maaten

Who trusts Fangping Wan overall

1. Sabrina Jaeger
2. Masashi Tsubaki
3. Mostafa Karimi
4. Mufti Mahmud
5. Mengying Sun
6. Fangda Gu
7. Simone Fulle
8. Kentaro Tomii
9. Maryam Bagherian
10. M. S. Kaiser

Papers from Fangping Wan's perspective

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

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

Their papers

• Deep learning with feature embedding for compound-protein interaction prediction (2016) — 77 citations
• MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities (2020) — 215 citations
• NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (2018) — 332 citations