Reverse engineering articles within Nature Communications

Featured

• Article
  24 July 2023 | Open Access

  A general model-based causal inference method overcomes the curse of synchrony and indirect effect

  Traditional causal inference methods struggle to distinguish direct causation from synchrony and indirect effects. Here, authors present GOBI that overcomes this by testing a general model’s ability to reproduce data, providing accurate and broadly applicable causality inference for complex systems.

  • Se Ho Park
  • , Seokmin Ha
  •  & Jae Kyoung Kim

• Article
  04 May 2023 | Open Access

  NetBID2 provides comprehensive hidden driver analysis

  It’s challenging to capture “hidden” drivers that may not be genetically-altered or differentially-expressed from omics data. Here the authors developed NetBID2, a comprehensive network-based toolbox with versatile features, enabling the integration of multi-omics data to expose such hidden drivers.

  • Xinran Dong
  • , Liang Ding
  •  & Jiyang Yu

• Article
  13 April 2022 | Open Access

  Data-driven learning how oncogenic gene expression locally alters heterocellular networks

  While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.

  • David J. Klinke II
  • , Audry Fernandez
  •  & Anika C. Pirkey

• Article
  25 May 2021 | Open Access

  Finding gene network topologies for given biological function with recurrent neural network

  Networks are useful ways to describe interactions between molecules in a cell, but predicting the real topology of large networks can be challenging. Here, the authors use deep learning to predict the topology of networks that perform biologically-plausible functions.

  • Jingxiang Shen
  • , Feng Liu
  •  & Chao Tang