Claudio Mirabello

Molecular Biology · 2014–2020

A researcher in the Science corpus: 5 papers; trusts 59 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 144 citations into it; #4,320 of 17,773 by trust here, #10,506 by citations
• Materials Chemistry — 75 citations into it; #9,667 of 11,513 by trust here, #9,927 by citations
• Computational Biology & Bioinformatics — 64 citations into it
• Spectroscopy — 18 citations into it
• Computational Theory and Mathematics — 17 citations into it; #11,288 of 12,182 by trust here, #8,294 by citations
• Artificial Intelligence — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Claudio Mirabello trusts overall

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

1. David T. Jones
2. Johannes Söding
3. Manfred J. Sippl
4. Michael Remmert
5. Gianluca Pollastri
6. Sheng Wang
7. Björn Wallner
8. Burkhard Rost
9. Ehsaneddin Asgari
10. Christopher Sander

Who trusts Claudio Mirabello overall

1. Shaun M. Kandathil
2. Carter J. Wilson
3. Joe G. Greener
4. Jinbo Xu
5. Mirko Torrisi
6. Wing Yiu Choy
7. David T. Jones
8. Andy M. Lau
9. Mikko Karttunen
10. Gianluca Pollastri

Papers from Claudio Mirabello's perspective

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

1. Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
2. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
6. ImageNet classification with deep convolutional neural networks
7. The Protein Data Bank.
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Gradient-based learning applied to document recognition

Their papers

• Methods for estimation of model accuracy in CASP12 (2017) — 32 citations
• rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments (2019) — 83 citations
• Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014) — 48 citations
• Critical assessment of protein intrinsic disorder prediction (2020) — 286 citations
• rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete (2018) — 11 citations