Arsenii O. Zinkevich

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

This study identifies DNA binding motifs for 98 human transcription factors using meSMiLE-seq, revealing methylation-sensitive binding preferences for some TFs.

The paper uses GHT-SELEX to show many human TFs have high intrinsic sequence specificity and complex DNA binding, often matching in vivo ChIP-seq results.

The paper presents a machine learning framework to predict RNA-protein interactions using eCLIP data and shows its limitations in extrapolating to proteins without such data.

The paper evaluates cross-platform DNA motif discovery tools using 4,237 experiments to identify reliable transcription factor binding motifs and highlights challenges in motif detection.

This paper introduces PARADE, a generative AI framework for designing mRNA UTRs with cell-type specificity, showing improved therapeutic efficacy in preclinical models.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

The paper introduces RibonanzaNet, a deep learning model trained on crowdsourced RNA chemical mapping data to predict RNA structure and related properties.

LegNet is a deep learning model for predicting gene expression from DNA sequences, outperforming existing models and enabling promoter sequence design.