Christopher M. Wilson

iModMix: Integrative Module Analysis for Multi-omics Data

iModMix identifies novel multi-omics associations by constructing data-driven modules without relying on biological annotations.

scSpatialSIM: a simulator of spatial single-cell molecular data

scSpatialSIM simulates spatial single-cell molecular data to evaluate spatial clustering methods, finding Ripleys K(r) best detects clustering across scenarios.

spatialGE: Quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics

The paper introduces spatialGE, a software tool for quantifying and visualizing tumor microenvironment heterogeneity using spatial transcriptomics data.

Efficient gradient boosting for prognostic biomarker discovery

This paper introduces Xsurv, an R package using XGB and LGB for survival analysis, showing it outperforms traditional methods in biomarker discovery for cancer prognosis.

Fenchel duality of Cox partial likelihood and its application in survival kernel learning

This paper introduces a dual form of the Cox partial likelihood loss using Fenchel duality, enabling efficient kernel learning for survival analysis with censored outcomes.

Wide and Deep Learning for Automatic Cell Type Identification

This paper applies wide deep learning to cell type classification, showing improved accuracy when models are trained on one cancer type and tested on another.

Multiple-kernel learning for genomic data mining and prediction

This paper introduces an R package for multiple kernel learning, showing improved classification by combining multiple data sources in ovarian cancer.