Author
Arlin Stoltzfus
National Institute of Standards and TechnologyORCID 0000-0002-0963-1357Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2015-2025 · peak 2/yrCo-Author Collaboration Network
10 top collaborators · 15 mutual co-author linksMutual co-authors in cluster
6 papers
Recent·Most cited
- Evolutionary BiologybioRxivPreprint✦ In-depth digest
Inference and visualization of complex genotype-phenotype maps with gpmap-tools
Carlos Martí‐Gómez, Juannan Zhou, Wei-Chia Chen…·David M. McCandlish
gpmap-tools uses Gaussian processes to infer and visualize genotype-phenotype maps with higher-order epistasis; across 262,144 Shine-Dalgarno variants it reveals register-specific peaks linked by three-nucleotide-offset ridges.
4 citations - Evolutionary BiologybioRxivPreprint
A fitness distribution law for amino-acid replacements
Mengyi Sun, Arlin Stoltzfus, David M. McCandlish
The paper analyzes over 350,000 amino acid replacements to show that fitness effects follow a maximum entropy distribution, with implications for mutational prediction and protein design.
1 citation - BioinformaticsEvolutionary BioinformaticsPeer-reviewed
Phylotastic: improving access to tree-of-life knowledge with flexible, on-the-fly delivery of trees
Văn Đức Nguyễn, Thanh Hai Nguyen, Abu Saleh Md Tayeen…·Arlin Stoltzfus
Phylotastic provides an accessible system for on-the-fly delivery of phylogenetic trees, enabling users to retrieve species phylogenies with minimal effort.
- Evolutionary BiologyMolecular Biology and EvolutionPeer-reviewed
On the causes of evolutionary transition:transversion bias
Arlin Stoltzfus, Ryan W. Norris
The paper tests whether conservative transitions are more favorable in protein evolution by analyzing mutation fitness effects, finding weak support for this hypothesis.
DOI 10.1093/molbev/msv274 - Evolutionary BiologybioRxivPreprint
Bayesian priors for tree calibration: Evaluating two new approaches based on fossil intervals
Ryan W. Norris, Cory L. Strope, David M. McCandlish, Arlin Stoltzfus
This paper introduces two new Bayesian prior methods for calibrating phylogenetic trees using fossil data and shows they improve divergence time accuracy.
DOI 10.1101/014340