Author

Nick Hengartner

Los Alamos National Laboratory

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Epidemiology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Organism:human· 1 Research Focus:phylogenetics / evolution· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Alyssa Schultz· 1 shared Ana Abecasis· 1 shared Daniel R. TauritziD· 1 shared Ethan O. Romero-Severson· 1 shared Inês Bártolo· 1 shared Ingo Bulla· 1 shared John Frederick BeckmanniD· 1 shared José M. Azevedo-Pereira· 1 shared

Activity by Year

2016-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioinformatics

Alyssa Schultz

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Benchmarking Molecular Methods of Protein Engineering and Surveying Protein Search Space to Bolster Artificial Intelligence-Based Designs

Sivakumar Ramamoorthy, Ruby Browning, Joy Brandt, Kirin Tisdale, Alyssa Schultz, Maria Victoria Liendro Dalinger, Nick Hengartner, Daniel R. Tauritz, John Frederick Beckmann

The paper benchmarks molecular methods for protein engineering and evaluates AI-based designs by comparing mutagenesis and phenotypic screening of a fluorophore.

bioRxiv · Oct 22, 2026 · PreprintBioinformatics

Inference of direction, diversity, and frequency of HIV-1 transmission using approximate Bayesian computation

Ethan O. Romero-Severson, Ingo Bulla, Nick Hengartner, Inês Bártolo, Ana Abecasis, José M. Azevedo-Pereira, Nuno Taveira, Thomas Leitner

This study uses approximate Bayesian computation to infer HIV-1 transmission direction, diversity, and frequency, finding that a single transmission model better explains the data than ongoing transmission.

bioRxiv · Aug 24, 2016 · PreprintEpidemiology