Author

Sándor Pongor

Pázmány Péter Catholic UniversityORCID 0000-0001-6022-4792Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:whole-genome sequencing· 3 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Balázs LigetiiD· 3 shared Balázs GyőrffyiD· 2 shared Ádám M. NagyiD· 1 shared András HorváthiD· 1 shared Babett Bodnár· 1 shared János JuhásziD· 1 shared János SzebeniiD· 1 shared Judit Juhász· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 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

3 shared papersBioinformatics

Balázs LigetiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

ProkBERT PhaStyle: Accurate Phage Lifestyle Prediction with Pretrained Genomic Language Models

Judit Juhász, Noémi Ligeti-Nagy, Babett Bodnár, János Juhász, Sándor Pongor, Balázs Ligeti

ProkBERT PhaStyle uses genomic language models to predict phage lifestyles, outperforming existing methods in accuracy and speed.

Bioinformatics Advances · Dec 26, 2024Peer-reviewedBioinformatics

CovidOutcome2: a tool for SARS-CoV2 mutation identification and for disease severity prediction

Regina Kalcsevszki, András Horváth, Balázs Győrffy, Sándor Pongor, Balázs Ligeti

CovidOutcome2 predicts disease severity from SARS-CoV-2 mutation profiles using autoML and deep neural networks, achieving ROC-AUC scores of 0.899 and 0.83 with and without patient age.

bioRxiv · Jul 1, 2022 · PreprintBioinformatics

COVIDOUTCOME - Estimating COVID Severity Based on Mutation Signatures in the SARS-CoV-2 Genome

Ádám M. Nagy, Balázs Ligeti, János Szebeni, Sándor Pongor, Balázs Győrffy

The paper uses machine learning to link SARS-CoV-2 mutations to COVID-19 severity, identifying 26 mutations and achieving high classification accuracy.

Database · Jan 1, 2021Peer-reviewedBioinformatics