Author

Leandro F. Moreno

Genmab (Netherlands)ORCID 0000-0002-2529-0855Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:statistical model· 3 Research Focus:gene regulatory network inference· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Approach:deep learning· 1

Frequent Co-Authors

Indu KhatriiD· 2 shared Iris C. R. M. KolderiD· 2 shared Saskia D. van AsteniD· 2 shared Anna DahlmaniD· 1 shared Anna WojtuszkiewicziD· 1 shared Attila CsalaiD· 1 shared Brandon W. HiggsiD· 1 shared Christiaan N. KlijniD· 1 shared

Activity by Year

2018-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersBioinformatics

Indu KhatriORCID

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3 papers

Recent·Most cited

A Cross-Study Multi-Organ Cell Atlas ofMacaca fascicularis Informed by Human Foundation Model Annotation: A Resource for Translational Target Assessment

Terezinha Maria de Souza, Joshua T. Gamse, Leandro F. Moreno, Marilijn van Rumpt, Gonzalo Nuñez-Moreno, Indu Khatri, Saskia D. van Asten, Nichal V. Khusial, Empar Baltasar Pérez, Ragini Adhav, Tamim Abdelaal, Anna Wojtuszkiewicz, Jorg J. A. Calis, Attila Csala, Anna Dahlman, , Iris C. R. M. Kolder

This paper creates a cross-species single-cell atlas of Macaca fascicularis using human reference labels to improve target qualification and toxicity interpretation in preclinical research.

bioRxiv · Mar 19, 2026 · PreprintBioinformatics

VIBE: An R-package for advanced RNA-seq data exploration, disease stratification and therapeutic targeting

Indu Khatri, Saskia D. van Asten, Leandro F. Moreno, Brandon W. Higgs, Christiaan N. Klijn, Francis Blokzijl, Iris C. R. M. Kolder

VIBE is an R package that enables pathway-guided RNA-seq analysis for disease stratification and therapeutic targeting in various disorders.

bioRxiv · Oct 4, 2023 · PreprintBioinformatics

The identification of DNA binding regions of the σ54 factor using artificial neural network

Leandro F. Moreno, Roberto Tadeu Raittz, Jeroniza Nunes Marchaukoski, Vinícius Almir Weiss, I. C. R. Santos-Wiess, P. A. B. Costa, Ricardo Voyceik, Liu Un Rigo

This study introduces a neural network model and tool to identify σ54 TFBSs with high sensitivity and specificity, enabling genome-wide analysis of σ54-regulated genes across bacterial species.

bioRxiv · Aug 17, 2018 · PreprintBioinformatics