Searching journal content for articles similar to Minnoye et al. 30 (12): 1815.

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  1. Method: Domain-adaptive neural networks improve cross-species prediction of transcription factor binding
    • Kelly Cochran,
    • Divyanshi Srivastava,
    • Avanti Shrikumar,
    • Akshay Balsubramani,
    • Ross C. Hardison,
    • Anshul Kundaje,
    • and Shaun Mahony
    Genome Res. March 2022 32: 512-523; Published in Advance January 18, 2022, doi:10.1101/gr.275394.121
    .... These auPRCs also show that our neural network approach can indeed outperform related methods on this task.Next, we sought to assess the cross-species performance of another state-of-the-art deep learning model trained on a related TF binding prediction task, distinct from our binary classification setup...
  2. Resource: Modeling transcriptional regulation of model species with deep learning
    • Evan M. Cofer,
    • João Raimundo,
    • Alicja Tadych,
    • Yuji Yamazaki,
    • Aaron K. Wong,
    • Chandra L. Theesfeld,
    • Michael S. Levine,
    • and Olga G. Troyanskaya
    Genome Res. June 2021 31: 1097-1105; Published in Advance April 22, 2021, doi:10.1101/gr.266171.120
    ...evolution. Nature 447: 714–719. doi:10.1038/nature05846 ↵Kelley DR. 2020. Cross-species regulatory sequence activity prediction. PLoS Comput Biol 16: e1008050. doi:10.1371/journal.pcbi.1008050 ↵Kelley DR, Snoek J, Rinn JL. 2016. Basset: learning the regulatory code of the accessible with deep convolutional...
    OPEN ACCESS ARTICLE
  3. Method: Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
    • Zeynep Kalender Atak,
    • Ibrahim Ihsan Taskiran,
    • Jonas Demeulemeester,
    • Christopher Flerin,
    • David Mauduit,
    • Liesbeth Minnoye,
    • Gert Hulselmans,
    • Valerie Christiaens,
    • Ghanem-Elias Ghanem,
    • Jasper Wouters,
    • and Stein Aerts
    Genome Res. June 2021 31: 1082-1096; Published in Advance April 8, 2021, doi:10.1101/gr.260851.120
    ...IP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin...
    OPEN ACCESS ARTICLE
  4. Method: Evidence of widespread, independent sequence signature for transcription factor cobinding
    • Manqi Zhou,
    • Hongyang Li,
    • Xueqing Wang,
    • and Yuanfang Guan
    Genome Res. February 2021 31: 265-278; Published in Advance December 10, 2020, doi:10.1101/gr.267310.120
    ...elusive what contributes to the in vivo TF cobinding landscape. In this study, we developed a machine learning algorithm to explore the contributors of the cobinding patterns. The algorithm substantially outperforms the state-of-the-field models for TF cobinding prediction. Game theory–based feature...
  5. Mini-Review: Large-scale genomic analysis of the domestic dog informs biological discovery
    • Reuben M. Buckley and
    • Elaine A. Ostrander
    Genome Res. June 2024 34: 811-821; Published in Advance July 2, 2024, doi:10.1101/gr.278569.123
    ...to map human noncoding regulatory elements within the canine , enhancing prediction of variant impacts in dogs (Hinrichs et al. 2006). However, the use of species pairwise alignment-based approaches for regulatory annotation assumes that conserved noncoding sequences have conserved epigenomic activity...
    OPEN ACCESS ARTICLE
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