Payam Dibaeinia

The paper analyzes the impact of CYP2C19 genotype on SSRI side effects and discontinuation rates in a large sample of individuals.

The paper argues that scaling model capacity alone is insufficient for Virtual Cells, emphasizing the need for contextual diversity and causal representation learning over data volume.

This study finds that higher polygenic risk scores for breast cancer are associated with longer survival in patients, suggesting a link between genetic predisposition and less aggressive tumors.

PRSformer predicts disease risk from large-scale genotypes using deep learning, outperforming linear models and summary-statistic methods in multi-ancestry cohorts.

The paper uses single-cell transcriptomics to show how genetic variants influence gene regulatory networks in honeybee brains, affecting aggression-related behaviors through specific cell populations and pathways.

The paper compares thermodynamics-based models and a CNN called CoNSEPT for predicting enhancer function in Drosophila, finding that complex models can suggest interpretable mechanisms.

SERGIO simulates single-cell gene expression data using gene regulatory networks, producing datasets comparable to experimental data for benchmarking analysis tools.