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MENTOR: Multiplex Embedding of Networks for Team-Based Omics Research.

Kyle A SullivanJ Izaak MillerAlice TownsendMallory MorganMatthew J LaneMirko Pavicic VenegasManesh ShahMikaela CashmanDaniel A Jacobson
Published in: bioRxiv : the preprint server for biology (2024)
While the proliferation of data-driven omics technologies has continued to accelerate, methods of identifying relationships among large-scale changes from omics experiments have stagnated. It is therefore imperative to develop methods that can identify key mechanisms among one or more omics experiments in order to advance biological discovery. To solve this problem, here we describe the network-based algorithm MENTOR - Multiplex Embedding of Networks for Team-Based Omics Research. We demonstrate MENTOR's utility as a supervised learning approach to successfully partition a gene set containing multiple ontological functions into their respective functions. Subsequently, we used MENTOR as an unsupervised learning approach to identify important biological functions pertaining to the host genetic architectures in Populus trichocarpa associated with microbial abundance of multiple taxa. Moreover, as open source software designed with scientific teams in mind, we demonstrate the ability to use the output of MENTOR to facilitate distributed interpretation of omics experiments.
Keyphrases
  • single cell
  • machine learning
  • high throughput
  • palliative care
  • genome wide
  • copy number
  • deep learning
  • microbial community
  • signaling pathway
  • dna methylation
  • neural network
  • real time pcr