• Smart computational exploratio...

Treffer: Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning.

Title:
Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning.
Authors:
Wrede F; Department of Information Technology, Uppsala University, Uppsala SE-75105, Sweden., Hellander A; Department of Information Technology, Uppsala University, Uppsala SE-75105, Sweden.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2019 Dec 15; Vol. 35 (24), pp. 5199-5206.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Gene Regulatory Networks*, Humans ; Software ; Supervised Machine Learning ; Workflow
References:
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Grant Information:
R01 EB014877 United States EB NIBIB NIH HHS
Entry Date(s):
Date Created: 20190530 Date Completed: 20200630 Latest Revision: 20201215
Update Code:
20250114
PubMed Central ID:
PMC6954658
DOI:
10.1093/bioinformatics/btz420
PMID:
31141124
Database:
MEDLINE

Weitere Informationen

Motivation: Discrete stochastic models of gene regulatory network models are indispensable tools for biological inquiry since they allow the modeler to predict how molecular interactions give rise to nonlinear system output. Model exploration with the objective of generating qualitative hypotheses about the workings of a pathway is usually the first step in the modeling process. It involves simulating the gene network model under a very large range of conditions, due to the large uncertainty in interactions and kinetic parameters. This makes model exploration highly computational demanding. Furthermore, with no prior information about the model behavior, labor-intensive manual inspection of very large amounts of simulation results becomes necessary. This limits systematic computational exploration to simplistic models.
Results: We have developed an interactive, smart workflow for model exploration based on semi-supervised learning and human-in-the-loop labeling of data. The workflow lets a modeler rapidly discover ranges of interesting behaviors predicted by the model. Utilizing that similar simulation output is in proximity of each other in a feature space, the modeler can focus on informing the system about what behaviors are more interesting than others by labeling, rather than analyzing simulation results with custom scripts and workflows. This results in a large reduction in time-consuming manual work by the modeler early in a modeling project, which can substantially reduce the time needed to go from an initial model to testable predictions and downstream analysis.
Availability and Implementation: A python-package is available at https://github.com/Wrede/mio.git.
Supplementary Information: Supplementary data are available at Bioinformatics online.
(© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)