Michal Linial

Molecular Biology · 2000–2021

A researcher in the Science corpus: 9 papers; trusts 60 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 169 citations into it; #1,186 of 17,773 by trust here, #2,245 by citations
• Artificial Intelligence — 53 citations into it; #8,498 of 35,345 by trust here, #6,517 by citations
• Computational Biology & Bioinformatics — 53 citations into it
• Natural Language Processing — 29 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Spectroscopy — 25 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Large Language Models — 21 citations into it

Whom Michal Linial trusts overall

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

1. Johannes Söding
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Tomas Mikolov
5. Yoshua Bengio
6. Alex Krizhevsky
7. Roded Sharan
8. Diederik P. Kingma
9. Yann LeCun
10. Richard Socher

Who trusts Michal Linial overall

1. Nir Friedman
2. Kevin Kaichuang Yang
3. Nadav Brandes
4. Ziv Bar-Joseph
5. Marco Scutari
6. Nicolas Le Novère
7. Wolfram Liebermeister
8. Tristan Bepler
9. Jack Kuipers
10. Dan Ofer

Papers from Michal Linial's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Long Short-Term Memory
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Using Bayesian Networks to Analyze Expression Data (2000) — 3,690 citations
• A large-scale evaluation of computational protein function prediction (2013) — 1,002 citations
• ProFET: Feature engineering captures high-level protein functions (2015) — 104 citations
• The language of proteins: NLP, machine learning & protein sequences (2021) — 336 citations
• Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space (2008) — 160 citations
• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• Novel Unsupervised Feature Filtering of Biological Data (2006) — 321 citations
• ProteinBERT: a universal deep-learning model of protein sequence and function (2021) — 979 citations
• Insights into social insects from the genome of the honeybee Apis mellifera (2006) — 1,446 citations