PhenoEncoder improves genomic data compression by integrating phenotype information, enhancing downstream classification performance in complex trait studies.

bioRxiv · Dec 6, 2024 · PreprintGenomics

Computing Linkage Disequilibrium Aware Genome Embeddings using Autoencoders

Gizem Taş, Timo Westerdijk, Eric O. Postma, Wouter van Rheenen, Mark K. Bakker, Kristel R. van Eijk, Maarten Kooyman, Ahmad Al Khleifat, Alfredo Iacoangeli, Nicola Ticozzi, Johnathan Cooper‐Knock, Marta Gromicho, Siddharthan Chandran, Karen E. Morrison, Pamela J. Shaw, , Marleen Balvert

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

Bioinformatics · May 17, 2024Peer-reviewedGenomics

OGRE: Overlap Graph-based metagenomic Read clustEring

Marleen Balvert, Xiao Ling Luo, Ernestina Hauptfeld, Alexander Schönhuth, Bas E. Dutilh

OGRE clusters metagenomic reads into species-specific bins efficiently for large datasets without computational limitations.

Bioinformatics · Aug 25, 2020Peer-reviewedBioinformatics

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

Bojian Yin, Marleen Balvert, Rick A. A. van der Spek, Bas E. Dutilh, Sander M. Bohté, Jan Herman Veldink, Alexander Schönhuth

This paper develops a deep learning approach to predict ALS from genotype by leveraging genome structure, showing better performance than other methods.

Bioinformatics · May 9, 2019Peer-reviewedGenetics