Shanfeng Zhu

Molecular Biology · 2010–2019

A researcher in the Science corpus: 8 papers; trusts 42 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Molecular Biology — 95 citations into it
• Computational Theory and Mathematics — 49 citations into it
• Artificial Intelligence — 41 citations into it; #32,656 of 35,345 by trust here, #21,878 by citations
• Computational Biology & Bioinformatics — 16 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Management Science and Operations Research — 13 citations into it
• Information Systems — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom Shanfeng Zhu trusts overall

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

1. Chih-Jen Lin
2. Mehmet Gönen
3. Yoshihiro Yamanishi
4. Kevin Bleakley
5. Lijuan Cao
6. Minoru Kanehisa
7. Robi Polikar
8. Robert P. Schumaker
9. Doulaye Dembélé
10. Teuvo Kohonen

Who trusts Shanfeng Zhu overall

1. S. Shokralla

Papers from Shanfeng Zhu's perspective

Ordered by how strongly the researchers Shanfeng Zhu 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. Long Short-Term Memory
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Dropout: a simple way to prevent neural networks from overfitting
9. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multiconstrained gene clustering based on generalized projections (2010) — 410 citations
• Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014) — 384 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019) — 41 citations
• Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014) — 64 citations
• GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017) — 174 citations
• AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification (2018) — 303 citations
• Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013) — 328 citations