Gianluca Pollastri

Molecular Biology · 1999–2020

A researcher in the Science corpus: 8 papers; trusts 78 researchers and is trusted by 70 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 99 citations into it; #866 of 17,773 by trust here, #4,989 by citations
• Computational Biology & Bioinformatics — 68 citations into it; #322 of 660 by trust here, #416 by citations
• Materials Chemistry — 55 citations into it; #557 of 11,513 by trust here, #4,696 by citations
• Computational Theory and Mathematics — 31 citations into it; #1,173 of 12,182 by trust here, #884 by citations
• Model Architectures — 16 citations into it
• Artificial Intelligence — 12 citations into it; #10,247 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Gianluca Pollastri trusts overall

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

1. Pierre Baldi
2. Burkhard Rost
3. W. Kabsch
4. David C. Jones
5. Christopher Sander
6. Stephen F. Altschul
7. David Baker
8. Michele Vendruscolo
9. Bruce Rannala
10. Gregory R. Grant

Who trusts Gianluca Pollastri overall

1. Jianlin Cheng
2. Pierre Baldi
3. Arlo Z. Randall
4. Mirko Torrisi
5. Zachary Chase Lipton
6. Jesse Eickholt
7. Ke Chen
8. Predrag Kukic
9. Matthew Spencer
10. Claudio Mirabello

Papers from Gianluca Pollastri's perspective

Ordered by how strongly the researchers Gianluca Pollastri 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. Long Short-Term Memory
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Dropout: a simple way to prevent neural networks from overfitting
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles (2002) — 809 citations
• Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules (2013) — 506 citations
• Porter: a new, accurate server for protein secondary structure prediction (2005) — 484 citations
• Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners (2002) — 156 citations
• Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014) — 48 citations
• Deep learning methods in protein structure prediction (2020) — 239 citations
• The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem (2003) — 228 citations
• Exploiting the past and the future in protein secondary structure prediction (1999)