Ping‐Han Hsieh

The paper analyzes gene regulatory networks across 9,000 tumors to identify recurrent PD-1 pathway dysregulation linked to patient outcomes.

This study integrates multi-omics data with gene regulatory networks to improve survival predictions in ten cancer types, showing improved associations with patient survival when GRNs are included.

The paper introduces SNAIL, a normalization method that reduces false-positive co-expression associations in RNA-sequencing data, improving downstream network analysis.

The paper develops a computational framework to identify regulatory heterogeneity in leiomyosarcoma by analyzing gene regulatory networks and validates findings in independent datasets.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

GenEpi detects gene-based epistasis using machine learning, outperforming existing methods on simulated data and identifying disease-related variants in Alzheimer's disease.

lionessR provides an R implementation of LIONESS for single-sample network reconstruction using any network method that outputs a weighted adjacency matrix, demonstrating its use on a bone cancer dataset.

This study evaluates how de novo transcriptome assembly quality affects RNA-Seq quantification accuracy and introduces a tool to assess quantification results.