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  1. No Access

    Article

    Current approaches to genomic deep learning struggle to fully capture human genetic variation

    Deep learning shows promise for predicting gene expression levels from DNA sequences. However, recent studies show that current state-of-the-art models struggle to accurately characterize expression variation ...

    Ziqi Tang, Shushan Toneyan, Peter K. Koo in Nature Genetics (2023)

  2. Article

    Open Access

    EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations

    Deep neural networks (DNNs) hold promise for functional genomics prediction, but their generalization capability may be limited by the amount of available data. To address this, we propose EvoAug, a suite of e...

    Nicholas Keone Lee, Ziqi Tang, Shushan Toneyan, Peter K. Koo in Genome Biology (2023)

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  3. No Access

    Article

    ETV6 dependency in Ewing sarcoma by antagonism of EWS-FLI1-mediated enhancer activation

    The EWS-FLI1 fusion oncoprotein deregulates transcription to initiate the paediatric cancer Ewing sarcoma. Here we used a domain-focused CRISPR screen to implicate the transcriptional repressor ETV6 as a uniqu...

    Yuan Gao, Xue-Yan He, **aoli S. Wu, Yu-Han Huang, Shushan Toneyan… in Nature Cell Biology (2023)

  4. No Access

    Article

    Evaluating deep learning for predicting epigenomic profiles

    Deep learning has been successful at predicting epigenomic profiles from DNA sequences. Most approaches frame this task as a binary classification relying on peak callers to define functional activity. Recentl...

    Shushan Toneyan, Ziqi Tang, Peter K. Koo in Nature Machine Intelligence (2022)