Automating parameter selection to avoid implausible biological pathway models.

ABSTRACT: A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions.

SUBMITTER: Magnano CS 

PROVIDER: S-EPMC7902638 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature