Rxivist logo

Predicting complex genetic phenotypes using error propagation in weighted networks

By El Mahdi El Mhamdi, Andrei Kucharavy, Rachid Guerraoui, Rong Li

Posted 21 Dec 2018
bioRxiv DOI: 10.1101/487348

Network-biology view of biological systems is a ubiquitous abstraction that emerged in the last two decades to allow a high-level understanding of principles governing them. However, the principles according to which biological systems are organized are still unclear. Here, we investigate if biological networks could be approximated as overlapping, feed-forward networks where the nodes have non-linear activation functions. Such networks have been shown to be universal approximators and their stability has been explored in the context of artificial neural networks. Mathematical formalization of this model followed by numerical simulations based on genomic data allowed us to accurately predict the statistics of gene essentiality in yeast and hence indicate that biological networks might be better understood as a distributed system, comprising potentially unreliable components.

Download data

  • Downloaded 611 times
  • Download rankings, all-time:
    • Site-wide: 57,421
    • In systems biology: 1,240
  • Year to date:
    • Site-wide: 110,393
  • Since beginning of last month:
    • Site-wide: 101,948

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

News