Author

David Čechák

Masaryk University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 5

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 4 Research Focus:differential expression· 2 Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:whole-genome sequencing· 2 Approach:benchmark study· 1

Frequent Co-Authors

Panagiotis AlexiouiD· 5 shared Katarína GrešováiD· 4 shared Dimosthenis TzimotoudisiD· 3 shared Vlastimil MartinekiD· 3 shared Eva MarsalkovaiD· 2 shared Petr ŠimečekiD· 2 shared Stephanie SammutiD· 2 shared Aitor Alberdi Escudero· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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5 shared papersBioinformatics

Panagiotis AlexiouORCID

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5 papers

Recent·Most cited

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea, Elissavet Zacharopoulou, Aitor Alberdi Escudero, Edward Blake, David Čechák, Luke Cassar, Alessandro Balestrucci, Panagiotis Alexiou

Agentomics is an autonomous system that generates ML models for biomedical tasks, outperforming existing agentic systems and human-engineered models on 11/20 datasets.

Bioinformatics · Jul 1, 2026Peer-reviewedBioinformatics

miRBind2 enables sequence-only prediction of miRNA binding and transcript repression

David Čechák, Dimosthenis Tzimotoudis, Stephanie Sammut, Katarína Grešová, Eva Marsalkova, David Farrugia, Panagiotis Alexiou

miRBind2 is a deep learning model that predicts miRNA binding and transcript repression from sequence, outperforming existing methods with fewer parameters.

Bioinformatics · Jun 18, 2026Peer-reviewedBioinformatics

miRBench: novel benchmark datasets for microRNA binding site prediction that mitigate against prevalent microRNA Frequency Class Bias

Stephanie Sammut, Katarína Grešová, Dimosthenis Tzimotoudis, Eva Marsalkova, David Čechák, Panagiotis Alexiou

The paper introduces miRBench, a benchmark dataset for microRNA binding site prediction that reduces miRNA frequency class bias, showing improved performance with a simple CNN model.

Bioinformatics · Apr 24, 2025Peer-reviewedBioinformatics

Genomic Benchmarks: A Collection of Datasets for Genomic Sequence Classification

Katarína Grešová, Vlastimil Martinek, David Čechák, Petr Šimeček, Panagiotis Alexiou

The paper introduces a collection of genomic sequence classification datasets and a baseline CNN model, distributed as a Python package to improve reproducibility in genomics machine learning.

BMC Genomic Data · May 1, 2023Peer-reviewedBioinformatics

Fine-Tuning Transformers For Genomic Tasks

Vlastimil Martinek, David Čechák, Katarína Grešová, Panagiotis Alexiou, Petr Šimeček

This study evaluates Transformer models for genomic sequence classification, comparing pre-trained and from-scratch approaches on benchmark datasets.

bioRxiv · Feb 7, 2022 · PreprintBioinformatics