Ziqi Tang

This paper introduces DEGU, a method that improves deep learning reliability in genomics by combining ensemble learning and knowledge distillation to capture both average predictions and uncertainty.

NTv3 is a foundation model that predicts genome function, annotates genes, and designs DNA sequences across species, outperforming existing models in multiple tasks.

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.

This study evaluates pre-trained DNA language models' ability to predict and interpret cell-type-specific functional genomics data, finding they do not outperform conventional machine learning approaches.

This paper introduces D3, a generative model for designing regulatory DNA with tunable activity, showing improved performance over baselines in sequence generation and activity prediction.

EvoAug improves genomic DNN generalization and interpretability through evolution-inspired data augmentations while preserving functional integrity.

This study evaluates deep learning models for predicting chromatin accessibility, showing that quantitative models outperform binary ones in generalization and interpretability.

The paper benchmarks neuronal cell-type annotation in scATAC-seq data using marker genes and introduces a deep learning model to predict chromatin accessibility from DNA sequence.