Arnor Ingi Sigurdsson

The paper introduces CLASTER, a deep learning model that predicts nascent transcription levels from chromatin landscape and 3D structure data, achieving high correlation with experimental measurements.

EIRsurvival predicts time-to-diagnosis using genetic and multi-omics data from large cohorts, achieving C-indices between 0.55 and 0.96.

The paper identifies non-linear genetic regulation of plasma proteins using a deep learning method, finding dominance and epistasis effects in large cohorts.

This paper introduces AAMB, a deep learning method for metagenomic binning that improves genome reconstruction by integrating sequence co-abundances and tetranucleotide frequencies, outperforming existing methods in completeness and diversity.

The paper introduces a deep learning framework for polygenic risk score prediction, showing improved performance over traditional methods for various diseases.

This study uses deep learning to predict blood and urine biomarkers from genetic data, outperforming linear methods and capturing non-linear and epistatic effects.