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Volume 2 Issue 7, July 2020

Volume 2 Issue 7

Decoding differential gene expression

Identifying the molecular mechanisms that control gene expression is essential for progress in basic and disease biology. Taskaki et al. develop a systems biology model, using deep learning to predict differential gene expression and mine the biological basis of the underlying generative processes.

See Taskaki et al.

Image: Shinya Tasaki, Rush University. Cover design: Karen Moore.

Editorial

  • Editorial |

    AI tools used in society often enhance inequality, affecting Black lives disproportionally. Addressing this issue will require more than technological solutions. Researchers and experts in the field are overwhelmingly white and need to engage and listen to those experiencing the harm.

Correspondence

Comment & Opinion

  • Comment |

    Artificial intelligence tools can help save lives in a pandemic. However, the need to implement technological solutions rapidly raises challenging ethical issues. We need new approaches for ethics with urgency, to ensure AI can be safely and beneficially used in the COVID-19 response and beyond.

    • Asaf Tzachor
    • Jess Whittlestone
    • Seán Ó hÉigeartaigh

News & Views

  • News & Views |

    An important task in system biology is to understand cellular processes through the lens of gene sets and their expression patterns. Machine learning can help, but genes form complex interaction networks, and levarging this information in machine learning applications requires a sophisticated data representation.

    • Jan Hoinka
    • Teresa M. Przytycka

Reviews

  • Perspective |

    Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.

    • Mattia Prosperi
    • Yi Guo
    • Jiang Bian

Research

  • Article |

    A goal of biology is to identify the molecular mechanisms that control differential gene expression. Tasaki et al. have developed a framework that integrates genomic data into a deep learning model of transcriptome regulations to predict multiple transcriptional effects in tissue- and person-specific transcriptomes.

    • Shinya Tasaki
    • Chris Gaiteri
    • Yanling Wang
  • Article |

    Gene sets can provide valuable information for gaining insight into disease mechanisms and cellular functions. In this paper, the authors use a Gaussian approach to represent gene sets and gene networks in a low-dimensional space, allowing for accurate prediction and decreased computational complexity.

    • Sheng Wang
    • Emily R. Flynn
    • Russ B. Altman
  • Article |

    Currently available quantum hardware is limited by noise, so practical implementations often involve a combination with classical approaches. Sels et al. identify a promising application for such a quantum–classic hybrid approach, namely inferring molecular structure from NMR spectra, by employing a range of machine learning tools in combination with a quantum simulator.

    • Dries Sels
    • Hesam Dashti
    • Eugene Demler
  • Article |

    Machine learning has become popular in solving complex optical problems such as recovering the input phase and amplitude for a specific pattern or image measured through a scattering medium. In a more challenging application, Rahmani et al. consider the problem of also producing desired outputs for such a nonlinear system when only some intensity-only measurements of example outputs are available. They develop a neural network approach that can ensure the transmission of images through a highly nonlinear system—a multimode fibre—with a 90% fidelity.

    • Babak Rahmani
    • Damien Loterie
    • Christophe Moser
  • Article |

    Deep learning methods can be a powerful part of digital pathology workflows, provided well-annotated training datasets are available. Tolkach and colleagues develop a deep learning model to recognize and grade prostate cancer, based on a convolution neural network and a dataset with high-quality labels at gland-level precision.

    • Yuri Tolkach
    • Tilmann Dohmgörgen
    • Glen Kristiansen

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