Sarah Ancheta

This study compares five RNA velocity methods across three datasets to evaluate their performance in predicting cellular states.

The paper introduces a temporal AI model, MaxToki, trained on extensive gene data to predict cell state changes across human aging and identifies age-modulating targets.

The paper introduces Plexus, a self-supervised model for capturing neuronal activity dynamics, and demonstrates its effectiveness in identifying genetic modifiers of neuronal activity in frontotemporal dementia.

The paper presents Zebrahub-Multiome, a single-cell multiomic atlas of zebrafish embryogenesis, revealing gene regulatory network dynamics and cis-regulatory elements across developmental stages.

Zebrafish embryos sequenced individually across ten developmental stages, paired with light-sheet lineage reconstruction, form an atlas and browser for in silico fate mapping.