Arnor Ingi Sigurdsson

Modeling nascent transcription from chromatin landscape and structure

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: Deep Learning-based time-to-event analysis on high-dimensional genotype and multi-omics data

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

Non-linear genetic regulation of the blood plasma proteome

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

Adversarial and variational autoencoders improve metagenomic binning

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.

Deep integrative models for large-scale human genomics

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

Improved prediction of blood biomarkers using deep learning

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