Ping Zhang

Molecular Biology · with Wang · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 39 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 58 citations into it; #7,289 of 17,773 by trust here, #8,280 by citations
• Artificial Intelligence — 53 citations into it; #29,590 of 35,345 by trust here, #21,316 by citations
• Computational Theory and Mathematics — 43 citations into it; #6,682 of 12,182 by trust here, #2,931 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Pharmacology — 15 citations into it; #288 of 1,112 by trust here, #618 by citations
• Natural Language Processing — 9 citations into it
• Health Information Management — 6 citations into it; #232 of 344 by trust here, #164 by citations

Whom Ping Zhang trusts overall

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

1. Assaf Gottlieb
2. Michael Kuhn
3. Nitesh V. Chawla
4. Yunmei Chen
5. Geoffrey E. Hinton
6. Jimeng Sun
7. Fei Wang
8. Santiago Vilar
9. Nicholas P. Tatonetti
10. Alex Krizhevsky

Who trusts Ping Zhang overall

1. Fei Wang
2. Wei Zhang
3. Marinka Zitnik
4. Anita M. Preininger
5. Xiaofeng He
6. Tengfei Ma
7. Wen Zhang
8. Cao Xiao
9. Monica Agrawal
10. Hongyuan Zha

Papers from Ping Zhang's perspective

Ordered by how strongly the researchers Ping Zhang 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. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)

Their papers

• Interpretable Drug Target Prediction Using Deep Neural Representation (2018) — 254 citations
• Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects (2015) — 233 citations
• Risk Prediction with Electronic Health Records: A Deep Learning Approach (2016) — 425 citations
• Graph embedding on biomedical networks: methods, applications and evaluations (2019) — 370 citations
• Systematic prediction of drug combinations based on clinical side-effects (2014) — 62 citations
• Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics (2014) — 119 citations