Jie Hou

Molecular Biology · with Cheng · 2015–2019

A researcher in the Science corpus: 9 papers; trusts 61 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 221 citations into it; #4,530 of 17,773 by trust here, #5,282 by citations
• Computational Biology & Bioinformatics — 111 citations into it; #286 of 660 by trust here, #370 by citations
• Materials Chemistry — 110 citations into it
• Computational Theory and Mathematics — 33 citations into it
• Artificial Intelligence — 12 citations into it
• Model Architectures — 8 citations into it; #1,287 of 2,278 by trust here, #1,601 by citations

Whom Jie Hou trusts overall

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

1. Jianlin Cheng
2. Yang Zhang
3. Sheng Wang
4. Stephen F. Altschul
5. Renzhi Cao
6. David C. Jones
7. Liam J. McGuffin
8. Geoffrey E. Hinton
9. Jeffrey Skolnick
10. Jesse Eickholt

Who trusts Jie Hou overall

1. Jinbo Xu
2. Mostafa Karimi
3. Bowen Jing
4. Jianlin Cheng
5. Roshan Rao
6. Claudio Mirabello
7. Mirko Torrisi
8. Pedro Hermosilla
9. Shaowen Zhu
10. Badri Adhikari

Papers from Jie Hou's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Dropout: a simple way to prevent neural networks from overfitting
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Rectified Linear Units Improve Restricted Boltzmann Machines
10. Going deeper with convolutions

Their papers

• DNCON2: improved protein contact prediction using two-level deep convolutional neural networks (2017) — 148 citations
• Estimation of model accuracy in CASP13 (2019) — 75 citations
• Improving Protein Fold Recognition by Deep Learning Networks (2015) — 114 citations
• Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019) — 104 citations
• ConEVA: a toolbox for comprehensive assessment of protein contacts (2016) — 23 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017) — 228 citations
• Analysis of several key factors influencing deep learning-based inter-residue contact prediction (2019) — 35 citations
• Deep convolutional neural networks for predicting the quality of single protein structural models (2019) — 19 citations