Surojit Biswas

Molecular Biology · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 10 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 198 citations into it; #3,346 of 17,773 by trust here, #3,494 by citations
• Computational Biology & Bioinformatics — 83 citations into it; #410 of 660 by trust here, #295 by citations
• Materials Chemistry — 60 citations into it
• Artificial Intelligence — 42 citations into it
• Genetics — 36 citations into it
• Computational Theory and Mathematics — 27 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it

Whom Surojit Biswas trusts overall

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

1. Mohammed Alquraishi
2. Philip A. Romero
3. David Manheim
4. Benjamin T. Porebski
5. Frances H. Arnold
6. Dougal Maclaurin
7. Geoffrey E. Hinton
8. M. Stiffler
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Surojit Biswas overall

1. Michael Jendrusch
2. E. C. Alley
3. Jesse Vig
4. Joshua Meier
5. Jan O. Korbel
6. Brian L. Hie
7. Ali Madani
8. Grigory Khimulya
9. Sam Sinai
10. Roshan Rao

Papers from Surojit Biswas's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unified rational protein engineering with sequence-based deep representation learning (2019) — 1,084 citations
• Low-N protein engineering with data-efficient deep learning (2020) — 433 citations
• Single-sequence protein structure prediction using language models from deep learning (2021) — 64 citations
• Unified rational protein engineering with sequence-only deep representation learning (2019) — 47 citations
• Single-sequence protein structure prediction using language models and deep learning (2022) — 358 citations
• Toward machine-guided design of proteins (2018) — 35 citations
• Phytochromes function as thermosensors in Arabidopsis (2016) — 786 citations