Author

Mayukh Kansari

RWTH Aachen UniversityORCID 0009-0000-3377-1934Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:long-read sequencing· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:graph neural network· 1 Approach:statistical model· 1

Frequent Co-Authors

Jan Claas BehrendsiD· 2 shared Maria FytaiD· 2 shared Tobias EnssleniD· 2 shared Chih‐Yuan LiniD· 1 shared Kyril KavetskyiD· 1 shared Marija DrndićiD· 1 shared Michael MierzejewskiiD· 1 shared Peijia WeiiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

9 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Jan Claas BehrendsORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 11, 2026

Nanopore event detection in a simple and adaptive way

Peijia Wei, Mayukh Kansari, Michael Mierzejewski…·Maria Fyta

This paper introduces a simple, fast, and adaptable cluster-based event detection algorithm for nanopore signals, showing improved efficiency and reduced noise in biological nanopore data.

DOI 10.64898/2026.05.07.723187

long-read sequencingstatistical model

𝕏 PostBluesky

BioengineeringbioRxivPreprintApr 30, 2026

Graph-based learning and read-out of nanopore translocation event signals

Mayukh Kansari, Tobias Ensslen, Jan Claas Behrends, Maria Fyta

This paper develops a graph-based framework for label-free detection of nanopore translocation events, showing it can predict event morphology across a range of voltages.

DOI 10.64898/2026.04.29.721625

long-read sequencingdeep learning

𝕏 PostBluesky