Yongge Li

MEGA-ODE reconstructs dynamic omics landscapes from sparse data, predicts unmeasured states, and prioritizes perturbations toward biological endpoints.

Sequential-GAM constructs the single-cell geometric 3D genome structure by estimating radial positions and hierarchical organization of chromosomes and genes.

IDEAL-Age is a deep learning framework that profiles immunological aging at single-cell resolution, showing improved predictive performance and revealing cellular contributions to aging processes.

The paper introduces πDIA-CLIP, a zero-shot framework for DIA proteomics data analysis that improves protein identification and reduces false positives across diverse datasets.

This study benchmarks zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction and finds they underperform a traditional HVG baseline in capturing complex temporal patterns.

UniCure predicts personalized cancer therapy response by integrating omics and chemical models, achieving high accuracy in drug response and combination effects across diverse data.

CREaTor models cis-regulatory patterns using attention mechanisms and RNA-seq/ChIP-seq data, outperforming existing methods in predicting cCRE-gene interactions across cell types.

Uni-RNA, a deep learning model pre-trained on a large RNA sequence corpus, is reported to lead existing methods on structural and functional prediction tasks after fine-tuning.