Simona Cocco

Molecular Biology · 2009–2020

A researcher in the Science corpus: 7 papers; trusts 37 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 121 citations into it; #7,668 of 17,773 by trust here, #8,381 by citations
• Computational Biology & Bioinformatics — 76 citations into it
• Genetics — 32 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Artificial Intelligence — 19 citations into it
• Materials Chemistry — 18 citations into it
• Statistics and Probability — 15 citations into it
• Statistical and Nonlinear Physics — 10 citations into it; #5,326 of 10,127 by trust here, #3,513 by citations

Whom Simona Cocco trusts overall

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

1. Kevin P. Murphy
2. Geoffrey E. Hinton
3. Magnus Ekeberg
4. Martin Weigt
5. B. Reva
6. Ruslan Salakhutdinov
7. Thomas A. Hopf
8. Eric R. Ziegel
9. Christopher Sander
10. Debora Marks

Who trusts Simona Cocco overall

1. Jérôme Tubiana
2. Thierry Mora
3. Shou-Wen Wang
4. J. Trinquier
5. Ivan V. Anishchenko
6. Wenxu Wang
7. William Bialek
8. Gasper Tkacik
9. H. C. Nguyen
10. Martin Weigt

Papers from Simona Cocco's perspective

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

Their papers

• Learning protein constitutive motifs from sequence data (2018) — 115 citations
• An evolution-based model for designing chorismate mutase enzymes (2020) — 297 citations
• Inverse statistical physics of protein sequences: a key issues review (2017) — 242 citations
• Large pseudocounts and L2-norm penalties are necessary for the mean-field inference of Ising and Potts models. (2014) — 23 citations
• From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction (2012) — 109 citations
• High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections. (2011) — 33 citations
• Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods (2009) — 187 citations