Slobodan Vucetic

Molecular Biology · 2003–2019

A researcher in the Science corpus: 12 papers; trusts 50 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 150 citations into it; #876 of 17,773 by trust here, #1,495 by citations
• Materials Chemistry — 62 citations into it; #1,319 of 11,513 by trust here, #3,462 by citations
• Artificial Intelligence — 33 citations into it
• Computational Biology & Bioinformatics — 33 citations into it
• Cell Biology — 18 citations into it
• Health Information Management — 14 citations into it
• Optimization & Training — 14 citations into it
• Model Architectures — 12 citations into it

Whom Slobodan Vucetic trusts overall

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

1. Vladimir N Uversky
2. A. Keith Dunker
3. Martin A. Riedmiller
4. Zoran Obradovic
5. C. Brown
6. Pedro Romero
7. Koby Crammer
8. Peter E. Wright
9. Leo Breiman
10. H. J. Dyson

Who trusts Slobodan Vucetic overall

1. Vladimir N Uversky
2. Avner Schlessinger
3. Predrag Radivojac
4. Fanchi Meng
5. A. Keith Dunker
6. Christopher J. Oldfield
7. Kang Peng
8. Bin Xue
9. Peter Tompa
10. Lukasz Kurgan

Papers from Slobodan Vucetic's perspective

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

Their papers

• A large-scale evaluation of computational protein function prediction (2013) — 1,002 citations
• Length-dependent prediction of protein intrinsic disorder (2006) — 915 citations
• Flavors of protein disorder (2003) — 388 citations
• Protein flexibility and intrinsic disorder (2004) — 342 citations
• Predicting intrinsic disorder from amino acid sequence (2003) — 429 citations
• DisProt: a database of protein disorder (2005) — 237 citations
• Optimizing Long Intrinsic Disorder Predictors with Protein Evolutionary Information (2005) — 471 citations
• Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions. (2007) — 532 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training (2012) — 200 citations
• Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018) — 200 citations
• Mixture Model for Multiple Instance Regression and Applications in Remote Sensing (2012) — 55 citations