Jianyang Zeng

Molecular Biology · 2013–2020

A researcher in the Science corpus: 7 papers; trusts 184 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 128 citations into it; #2,503 of 17,773 by trust here, #7,657 by citations
• Computational Theory and Mathematics — 101 citations into it; #2,329 of 12,182 by trust here, #2,060 by citations
• Artificial Intelligence — 52 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it
• Computational Chemistry & Drug Discovery — 36 citations into it
• Materials Chemistry — 29 citations into it
• Computational Biology & Bioinformatics — 23 citations into it
• Pharmacology — 21 citations into it; #99 of 1,112 by trust here, #546 by citations

Whom Jianyang Zeng trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Ilya Sutskever
5. Alex Krizhevsky
6. Tomas Mikolov
7. Max Welling
8. Yoshihiro Yamanishi
9. Hugo Larochelle
10. Yann LeCun

Who trusts Jianyang Zeng overall

1. Mostafa Karimi
2. Maryam Bagherian
3. Sabrina Jaeger
4. Masashi Tsubaki
5. Mufti Mahmud
6. Di Wu
7. Mengying Sun
8. Kang Li
9. Xing Chen
10. Yunan Luo

Papers from Jianyang Zeng's perspective

Ordered by how strongly the researchers Jianyang Zeng 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Predicting drug-target interactions using restricted Boltzmann machines (2013) — 202 citations
• Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach (2015) — 220 citations
• 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
• A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017) — 786 citations
• DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM (2016) — 194 citations
• NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (2018) — 332 citations