Ming Fan

Molecular Biology · with Li · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 24 citations into it
• Computational Biology & Bioinformatics — 8 citations into it

Whom Ming Fan trusts overall

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

1. Kuo-Chen Chou

Papers from Ming Fan's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. Dropout: a simple way to prevent neural networks from overfitting
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
6. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes (Bioinform.)
7. EzyPred: a top-down approach for predicting enzyme functional classes and subclasses.
8. The I-TASSER Suite: protein structure and function prediction
9. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
10. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

Their papers

• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017) — 268 citations