Yoshihiro Yamanishi

Molecular Biology · 2004–2010

A researcher in the Science corpus: 7 papers; trusts 32 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 60 citations into it; #825 of 17,773 by trust here, #2,461 by citations
• Computational Theory and Mathematics — 23 citations into it; #1,037 of 12,182 by trust here, #869 by citations
• Artificial Intelligence — 16 citations into it
• Computational Biology & Bioinformatics — 10 citations into it
• Pharmacology — 9 citations into it
• Statistical Learning Theory — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Yoshihiro Yamanishi trusts overall

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

1. Minoru Kanehisa
2. Bernhard Schölkopf
3. Christopher M. Dobson
4. Masahiro Hattori
5. Andrew Y. Ng
6. Susumu Goto
7. Shotaro Akaho
8. Kevin Bleakley
9. Michael I. Jordan
10. Yuhai Wu

Who trusts Yoshihiro Yamanishi overall

1. Mehmet Gönen
2. Fangping Wan
3. Hao Ding
4. Kevin Bleakley
5. Jianyang Zeng
6. Yuhao Wang
7. Maryam Bagherian
8. Céline Brouard
9. Xiaodong Zheng
10. Fantine Mordelet

Papers from Yoshihiro Yamanishi's perspective

Ordered by how strongly the researchers Yoshihiro Yamanishi 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• KEGG for linking genomes to life and the environment (2007) — 6,544 citations
• Supervised prediction of drug–target interactions using bipartite local models (2009) — 610 citations
• Prediction of drug–target interaction networks from the integration of chemical and genomic spaces (2008) — 1,081 citations
• Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework (2010) — 457 citations
• Protein network inference from multiple genomic data: a supervised approach (2004) — 259 citations
• KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways (2009) — 68 citations
• Supervised Graph Inference (2004) — 102 citations