Jacob C. Kimmel

This study investigates how transient reprogramming affects somatic cell identity and aging in mice, finding that it can restore youthful gene expression but also suppress identity programs.

scNym transfers cell identity annotations across single-cell experiments using semi-supervised adversarial neural networks, outperforming existing methods.

The paper investigates age-related changes in muscle progenitors by analyzing single-cell RNA-seq data from young and aged mice, finding that differentiation amplifies age-related changes in progenitors but masks some changes.

This paper explores using modified VAE objectives to improve interpretability of latent representations in scRNA-seq data, finding better alignment with generative factors but reduced clustering performance.

This study finds that aged murine muscle stem cells have delayed activation kinetics but retain a youthful activation trajectory, suggesting changes in state transition dynamics contribute to aging.

This study uses single-cell RNA-seq to analyze aging in mice, finding that cell identity has a greater influence on aging trajectories than tissue environment.

This paper develops CNN and LSTM models to classify and predict cell motility, achieving performance comparable to hand-engineered features and showing utility in predicting muscle stem cell motility.