Jiawei Luo

HapFold improves haplotype reconstruction for diploid genomes by combining graph-based and sequence-based scaffolding methods, achieving high accuracy and contiguity.

This paper introduces HAST, a framework for spatial transcriptomics that adapts graph learning scales to tissue heterogeneity, improving spatial structure identification and tumor region characterization.

LaCONIC improves cancer survival prediction by integrating multi-omics data with gene regulatory networks and cancer subtypes, achieving better performance and interpretability.

ConvNTC predicts disease-related miRNA pairs and cell-related drug pairs by modeling triplet network connections with a novel convolutional neural tensor completion approach.

MiRGraph identifies microRNA-target interactions by integrating sequence and regulatory network data, outperforming existing methods in prediction accuracy and strength estimation.

Informeasure quantifies nonlinear dependence in biological networks using information theory measures, providing an R/Bioconductor package with multiple estimators for bivariate and trivariate analysis.

The paper quantifies synergistic and competitive miRNA interactions using multivariate information measurement, finding that neighboring and overlapping binding sites on mRNA lead to synergistic and competitive effects, respectively.

This paper introduces GCATSL, a graph contextualized attention network for predicting synthetic lethality in human cancers, outperforming existing methods on multiple datasets.