Author

Aaron Karlsberg

University of Southern California

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

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:benchmark study· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:imaging· 1 Assay:metagenomics· 1

Frequent Co-Authors

Eleazar EskiniD· 2 shared Lana S. MartiniD· 2 shared Serghei ManguliD· 2 shared Alex ZelikovskyiD· 1 shared Brian L. HilliD· 1 shared Caitlin Loeffler· 1 shared David KoslickiiD· 1 shared Douglas W. YaoiD· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersBioinformatics

Eleazar EskinORCID

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Author profile →

3 papers

Recent·Most cited

Predictive Deep Learning Model for Neural Vessel Occlusion

Aaron Karlsberg, Sandy Kim, Nima Zaghari, Lu Li

This paper develops a deep learning model to predict neural vessel occlusion sites from cerebral angiograms, achieving high accuracy and intersection over union metrics.

bioRxiv · Sep 27, 2023 · PreprintNeuroscience

Improving the usability and comprehensiveness of microbial databases

Caitlin Loeffler, Aaron Karlsberg, Lana S. Martin, Eleazar Eskin, David Koslicki, Serghei Mangul

The paper evaluates existing microbial databases and proposes guidelines for creating a unified reference database to enhance the quality and comprehensiveness of metagenomics research.

BMC Biology · Apr 7, 2020Peer-reviewedBioinformatics

Benchmarking of computational error-correction methods for next-generation sequencing data

Keith Mitchell, Jaqueline Brito, Igor Mandric, Qiaozhen Wu, Sergey Knyazev, Sei Chang, Lana S. Martin, Aaron Karlsberg, Ekaterina Sergeevna Gerasimov, Russell Littman, Brian L. Hill, Nicholas C. Wu, Harry Taegyun Yang, Kevin Hsieh, Linus Chen, , Serghei Mangul

This study benchmarks error-correction methods for next-generation sequencing data, finding significant variation in performance across datasets with no single method being optimal overall.

Genome biology · Mar 17, 2020Peer-reviewedBioinformatics