Author

Sofia Tapanelli

Imperial College LondonORCID 0000-0003-0714-8668Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Organism:multiple species· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Astrid HoermanniD· 1 shared Ellen K. G. Masters· 1 shared Eunice O. AdedejiiD· 1 shared Ezekiel Femi AdebiyiiD· 1 shared George K. ChristophidesiD· 1 shared Giuseppe Del CorsanoiD· 1 shared Gregor BucheriD· 1 shared Jürgen DönitziD· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperSynthetic Biology

Astrid HoermannORCID

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2 papers

Recent·Most cited

BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedOct 4, 2021

Identifying essential genes across eukaryotes by machine learning

Thomas Beder, Olufemi Aromolaran, Jürgen Dönitz…·Rainer Koenig

The paper uses machine learning to predict essential genes across eukaryotes, achieving high accuracy and validating predictions in Tribolium castaneum.

22 citations

humanmouse

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Synthetic BiologyeLifePeer-reviewedApr 12, 2021

Converting endogenous genes of the malaria mosquito into simple non-autonomous gene drives for population replacement

Astrid Hoermann, Sofia Tapanelli, Paolo Capriotti…·Nikolai Windbichler

This paper converts endogenous mosquito genes into non-autonomous gene drives for malaria control by using native regulatory sequences to express antimalarial molecules and guide-RNAs.

62 citations

experimental assay

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