Denghui Liu

AutoGenome is an automated deep learning framework for genomic profiling data that uses a residual fully-connected neural network and AutoML to train high-performance models.

AutoGGN integrates multi-omics data and molecular interaction networks using graph convolutional neural networks, achieving better performance in cancer and single-cell classification tasks.

Fast-Bonito improves the speed of nanopore sequencing basecalling while maintaining or slightly improving accuracy compared to the original Bonito.

AutoGenome V2 integrates multi-omics data using a novel multimodal deep learning approach, achieving state-of-the-art results in four tasks.