Maria D. Chikina

DataRemix is a normalization method that enhances biological signal detection in gene expression data by tuning hidden factors, outperforming existing methods without external knowledge.

ssNPA identifies sample-specific gene network deregulation by learning causal graphs from control data, outperforming existing methods in liver development single-cell RNA-seq analysis.

PLIER improves pathway inference and cell proportion analysis in gene expression data, enhancing replicability and biological insight in trans-eQTL studies.

The paper uses evolutionary rate covariation analysis to identify genes coevolving with Drosophila E-cadherin, revealing novel regulators of cell adhesion and actin dynamics.

The paper shows that enhancer-promoter interaction prediction models can achieve competitive performance without capturing the underlying mechanism, highlighting issues with overfitting and cross-validation.

The paper develops an attention-based neural network to predict enhancer-promoter interactions using DNA sequences and identifies key transcription factor binding sites.