Author

A. B. Gussow

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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Microbiology· 4

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:metagenomics· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1

Frequent Co-Authors

E. V. Koonin· 4 shared N. Auslander· 3 shared Y. I. Wolf· 3 shared S. Benler· 2 shared D. Antipov· 1 shared F. Zhang· 1 shared G. Faure· 1 shared M. Raykov· 1 shared

Activity by Year

2020-2020 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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4 shared papersMicrobiology

E. V. Koonin

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Author profile →

4 papers

Recent·Most cited

Prediction of the virus incubation period for COVID-19 and future outbreaks

A. B. Gussow, N. Auslander, Y. I. Wolf, E. V. Koonin

The paper predicts the upper limit of the incubation period for viruses like SARS-CoV-2 using genomic features to aid in outbreak control.

BMC Biology · Nov 30, 2020Peer-reviewedMicrobiology

Seeker: Alignment-free identification of bacteriophage genomes by deep learning

N. Auslander, A. B. Gussow, S. Benler, Y. I. Wolf, E. V. Koonin

Seeker identifies unknown bacteriophages in metagenomic data using deep learning without relying on sequence similarity to known phages.

Nucleic Acids Research · Oct 12, 2020Peer-reviewedMicrobiology

Thousands of previously unknown phages discovered in whole-community human gut metagenomes

S. Benler, N. Yutin, D. Antipov, M. Raykov, S. A. Shmakov, A. B. Gussow, P. A. Pevzner, E. V. Koonin

The paper identifies 3,738 new phage genomes from human gut metagenomes, classifying them into three new candidate families and revealing novel phage-host interaction mechanisms.

bioRxiv · Oct 7, 2020Microbiology

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses

N. Auslander, A. B. Gussow, G. Faure, Y. I. Wolf, F. Zhang, E. V. Koonin

This study identifies genomic features distinguishing highly pathogenic SARS-CoV-2, SARS-CoV, and MERS-CoV from less pathogenic coronaviruses using comparative genomics and machine learning.

bioRxiv · Apr 5, 2020Microbiology