Jingyi Li

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

RESEPT uses deep learning to visualize and characterize tissue architecture from spatial transcriptomics data, revealing pathological structures in Alzheimer's and glioblastoma samples.

Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells

The paper introduces RiboCalc, a model that quantitatively predicts transcript coding ability in human cells, revealing context-dependent coding transcripts.

Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network

The paper introduces a variable-convolutional layer for motif identification in omics data, showing improved performance over standard convolutional layers.

A novel framework for single-cell Hi-C clustering based on graph-convolution-based imputation and two-phase-based feature extraction

This paper introduces ScHiC-Rep, a framework for single-cell Hi-C clustering that improves imputation and feature extraction, outperforming existing methods on human and mouse datasets.

TIPS: Trajectory Inference of Pathway Significance through Pseudotime Comparison for Functional Assessment of single-cell RNAseq Data

TIPS assesses pathway and transcription factor contributions to developmental trajectories in scRNAseq data through pseudotime comparison and provides visualization tools.