Author

Ioanna‐Theoni Vourlaki

Institute of Agrifood Research and TechnologyORCID 0000-0002-3381-5287Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Genomics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:cell type annotation· 1 Organism:chicken / avian· 1 Approach:deep learning· 1 Assay:metagenomics· 1 Organism:plant (other)· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Antônio ReverteriD· 1 shared David Anthony KennyiD· 1 shared David N KellyiD· 1 shared Diego Pablo MorgaviiD· 1 shared Fuyong LiiD· 1 shared Ilma TapioiD· 1 shared Itzhak MizrahiiD· 1 shared Le Luo GuaniD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperMicrobiology

Antônio ReverterORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

MicrobiologyThe ISME JournalPeer-reviewedJan 1, 2026

Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds

Ioanna‐Theoni Vourlaki, Ori Furman, Ilma Tapio…·Yuliaxis Ramayo‐Caldas

This study identifies Ruminosignatures in cattle rumen microbiota linked to methane emissions and feed efficiency across breeds and geographies.

DOI 10.1093/ismejo/wrag185

chicken / avianmetagenomics

𝕏 PostBluesky

GenomicsbioRxivPreprintJan 18, 2024

Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants

Ioanna‐Theoni Vourlaki, Sebastian Ernesto Ramos-Onsins, Miguel Pérez-enciso A, Raúl Castanera

This study evaluates deep learning for predicting rice traits using structural and single-nucleotide genomic variants, finding that deep learning models outperform Bayesian models in 75% of cases.

DOI 10.1101/2024.01.18.576088

plant (other)whole-genome sequencing

𝕏 PostBluesky