Irina Voineagu

This study evaluates deconvolution methods for human brain transcriptome data, finding that cell-type signatures impact accuracy more than methods, and partial methods outperform complete ones.

This study examines circRNA evolution across primates, finding that expression profiles diverge rapidly between species but some circRNAs show long-term neural conservation.

This study benchmarks dimensionality reduction methods in scRNA-seq data using internal validation measures and DBSCAN, identifying LDA and PHATE as high-performing algorithms.

The paper characterizes circRNA expression in human brain samples, identifies novel circRNAs, and finds a co-expression module upregulated in autism.