Yue Wang

QWENDY infers gene regulatory networks from single-cell gene expression data across four time points, outperforming existing methods on experimental data but not synthetic data.

TRENDY improves gene regulatory network inference by integrating transformer models into the WENDY method, showing better performance on simulated and experimental data.

WENDY infers gene regulatory networks from single-cell gene expression data with time-course interventions by modeling covariance dynamics, showing good performance on synthetic and experimental data.

The paper develops a method to infer autoregulation in gene expression using variance-to-mean ratios and applies it to experimental data to identify potential autoregulated genes.

The paper addresses the problem of identifying transposable genes with different orders across individuals by transforming it into a longest common subsequence problem and proposing algorithms for different scenarios.