Eirini Arvaniti

AlphaGenome Atlas: in silico mutagenesis of the entire human genome improves prioritization and interpretation of non-coding variants

AlphaGenome Atlas predicts regulatory effects of non-coding variants across the human genome, improving their prioritization and interpretation.

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

AlphaGenome predicts functional genomic measurements from DNA sequences with high resolution and accuracy across multiple modalities, outperforming existing methods in variant effect prediction.

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

This study develops a deep learning model to automate Gleason grading of prostate cancer tissue microarrays, achieving agreement with pathologists and expert-level patient stratification.

Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series

This paper introduces a supervised learning method using synthetic data to reconstruct gene regulatory networks from experimental time-series, showing superior performance compared to unsupervised methods.

Sensitive detection of rare disease-associated cell subsets via representation learning.

This paper introduces CellCnn, a representation learning method to detect rare disease-associated cell subsets from single-cell data, identifying subsets with frequencies as low as 0.005%.