Kentaro Tomii

Molecular Biology · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 27 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 77 citations into it; #10,745 of 17,773 by trust here, #7,574 by citations
• Materials Chemistry — 31 citations into it
• Computational Theory and Mathematics — 27 citations into it; #8,819 of 12,182 by trust here, #2,689 by citations
• Computational Biology & Bioinformatics — 20 citations into it
• Artificial Intelligence — 12 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Computational Chemistry & Drug Discovery — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Kentaro Tomii trusts overall

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

1. Robert C. Edgar
2. Kazutaka Katoh
3. Yoon Kim
4. Diederik P. Kingma
5. Mehmet Gönen
6. Geoffrey J. Barton
7. Dzmitry Bahdanau
8. Blockin Blockingene
9. Fangping Wan
10. Yoshua Bengio

Who trusts Kentaro Tomii overall

1. Wen Torng
2. Shuangjia Zheng

Papers from Kentaro Tomii's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment (2016) — 130 citations
• Application of the MAFFT sequence alignment program to large data—reexamination of the usefulness of chained guide trees (2016) — 320 citations
• Parallelization of MAFFT for large-scale multiple sequence alignments (2018) — 893 citations
• Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences (2018) — 630 citations
• MitoFates: Improved Prediction of Mitochondrial Targeting Sequences and Their Cleavage Sites (2015) — 508 citations