Xun Gu

ORIGAMI reconstructs cis-regulatory networks using multi-omics data and deep learning, revealing synergistic transcription control and disease-associated rewiring.

CHORD integrates cross-species single-cell transcriptomic data to learn gene, cell, and cell-type representations, enabling cell-type annotation and cross-species comparison of developmental and phenotypic patterns.

TopicVI identifies context-specific gene programs in single-cell and spatial transcriptomics data by integrating biological knowledge with data-driven refinement.

This paper studies the allele-age problem under selection-duality by analyzing mutation age profiles at different neutrality bounds in population genetics.

This paper theoretically analyzes the fixation time of mutations under fluctuating selection among individuals and finds it is significantly shorter than strict neutrality.

This paper introduces the FSI theory, showing that fluctuating selection among individuals affects molecular evolution, leading to new neutrality concepts and reinterpretations of substitution rates in mammals.

This paper introduces a finite-state maximum likelihood approximation to improve genome phylogeny inference by reducing computational burden while accounting for gene duplication, loss, and horizontal transfer.

The paper introduces Fluctuating Selection among Individuals (FSI) as a new form of genetic drift, showing it dominates molecular evolution in most metazoan species.

Spanve is a nonparametric statistical method that identifies spatially variable genes in spatial transcriptomic data with fewer false positives, enabling better downstream analysis.

TSNAdb v2.0 updates the tumor-specific neoantigen database with stricter identification criteria, predicted neoantigens from three mutation types, and experimentally validated neoantigens classified by evidence.