Gene expression and cancer classification

MALER: a web server to build, evaluate, and apply machine learning models for numerical biomedical data analysis

A statistical framework for system-level synthesis

This paper introduces a statistical framework for system-level synthesis that learns and predicts biological processes from high-dimensional data while identifying participating features.

Variable sparsity non-negative matrix and tensor factorization algorithms and their applications in cancer genomics

This paper introduces variable-sparsity matrix and tensor factorization algorithms for cancer genomics to improve biological interpretability and modeling of cancer subtypes and pathways.

2,234 attempts to harmonize a multi-platform B-cell lymphoma bulk transcriptomic dataset: ComBat is superior by cross-batch prediction quality

The paper benchmarks 2,234 transcriptomic harmonization approaches on B-cell lymphoma data, finding ComBat excels in cross-batch prediction quality.

Surrogate variables and the risk of overfitting in neuropsychiatric gene expression analyses

This study shows that surrogate variable analysis (SVA) can lead to overfitting and reduced cross-cohort reproducibility in neuropsychiatric gene expression analyses.

'broadSeq', a benchmarking tool to contrast differential gene expression methods and identify when genomic neighbourhood matters

broadSeq benchmarks differential gene expression methods and shows DELocal identifies distinct genes by considering genomic neighbourhood, particularly for highly expressed genes.

Exploring transcriptomic and genomic latent variable correction approaches in differential expression analysis.

This paper evaluates combining expression-based surrogate variables and genotype-based principal components in differential expression analysis, finding the combined approach improves replicability and biological recall in ALS datasets.