Christopher Frederik Blum

The paper introduces SegmentNT, a DNA foundation model for genome annotation, achieving state-of-the-art performance in identifying 14 genic and regulatory elements across species.

ChatNT is a conversational agent that understands biological sequences and achieves state-of-the-art results on genomics tasks while being accessible to non-coders.

This paper designs bi-stable RNA molecules using deep learning by optimizing sequences through gradient-guided search based on superimposed target structures.

This paper introduces a deep learning method to predict RNA 3D structures from sequences, achieving new benchmarks in simulated blind tests.

This paper introduces circular filters for CNNs to improve motif inference from ChIP-seq data by enabling non-truncated and interpretable motif learning.

This study develops tools to quantify limits in inferring transcriptional networks from gene knockout screens and introduces an unbiased, scalable method for network inference.

The paper develops PGExpress, a tool to predict translation efficiency in E. coli using mRNA sequence features, achieving 74% accuracy and 0.81 AUC.