CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learning

By Masatoshi Hamanaka, Kei Taneishi, Hiroaki Iwata, Jun Ye, Jianguo Pei, Jinlong Hou, Yasushi Okuno. 2017

64 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Zhiyong Lu
2. Brett K. Beaulieu-Jones
3. Amir Hussain
4. Mufti Mahmud
5. Yanjun Qi
6. Jinbo Xu
7. M. S. Kaiser
8. Casey S. Greene

In Molecular Biology

1. Casey S. Greene
2. Yifan Peng
3. Brett K. Beaulieu-Jones
4. G. Way
5. Amr M. Alexandari
6. Zhiyong Lu
7. Avanti Shrikumar
8. S. Joshua Swamidass

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Some have also chosen to use standard central processing unit (CPU) implementations rather than sacrifice network size or performance [385].”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “2B. Tested over two CPI datasets, a DNN based method (DNN*) achieved superior prediction accuracy (93.2% in dataset1 and 93.8% in dataset2) compared to other methods based on RF (83.9% and 86.6%), LR (88.3% and 89.9% using LR2), and SVM (88.7% and 90.3% using SVM3) [88].”