Author

Lucero Mendoza‐Maldonado

Hospital Civil de GuadalajaraORCID 0000-0002-0088-2993Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:virus· 2 Approach:machine learning (classical)· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Alberto Paolo TondaiD· 3 shared Alejandro Lopez‐RinconiD· 3 shared Aletta D. KraneveldiD· 3 shared Carmina Angelica Perez-RomeroiD· 3 shared Eric ClaasseniD· 3 shared Johan GarsseniD· 3 shared Daphne G.J.C. MuldersiD· 1 shared Etienne CoziD· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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3 shared papersBioinformatics

Alberto Paolo TondaORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJan 21, 2022

SARS-CoV-2 Omicron Variant AI-based Primers

Carmina Angelica Perez-Romero, Alberto Paolo Tonda, Lucero Mendoza‐Maldonado…·Alejandro Lopez‐Rincon

This paper uses AI to develop a primer set for the SARS-CoV-2 Omicron variant, validated in silico and preliminarily in lab tests.

DOI 10.1101/2022.01.21.475953

machine learning (classical)

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BioinformaticsbioRxivPreprintJan 20, 2021

Design of Specific Primer Sets for the Detection of SARS-CoV-2 Variants of Concern B.1.1.7, B.1.351, P.1, B.1.617.2 using Artificial Intelligence

Carmina Angelica Perez-Romero, Alberto Paolo Tonda, Lucero Mendoza‐Maldonado…·Alejandro Lopez‐Rincon

The paper designs specific primer sets for SARS-CoV-2 variants using AI, showing high accuracy and potential for multiplexed testing.

18 citations

virusdeep learning

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BioinformaticsScientific ReportsPeer-reviewedJan 13, 2021

Design of Specific Primer Set for Detection of B.1.1.7 SARS-CoV-2 Variant using Deep Learning

Alejandro Lopez‐Rincon, Alberto Paolo Tonda, Lucero Mendoza‐Maldonado…·Aletta D. Kraneveld

The paper designs a specific primer set for detecting the B.1.1.7 SARS-CoV-2 variant using deep learning, achieving >99% accuracy with in-silico validation.

133 citationstop 2%

virusdeep learning

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