S. Kan
Molecular Biology · 2019
A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Molecular Biology — 19 citations into it; #17,011 of 17,773 by trust here, #13,284 by citations
- Computational Biology & Bioinformatics — 9 citations into it
Whom S. Kan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts S. Kan overall
Papers from S. Kan's perspective
Ordered by how strongly the researchers S. Kan trusts endorse each paper. Same corpus, a different order for every researcher.
- Exploring protein fitness landscapes by directed evolution
- Scikit-learn: Machine Learning in Python
- Machine learning - a probabilistic perspective
- Protein stability promotes evolvability.
- De novo protein design: fully automated sequence selection.
- Robustness–epistasis link shapes the fitness landscape of a randomly drifting protein
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- Adaptation in protein fitness landscapes is facilitated by indirect paths
- Variational auto-encoding of protein sequences
- Machine learning: Trends, perspectives, and prospects
Their papers
- Machine learning-assisted directed protein evolution with combinatorial libraries (2019) — 515 citations