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A biological network-based regularized artificial neural network model for robust phenotype prediction from gene

Tianyu Kang1, Wei Ding1, Luoyan Zhang1

  • 1Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, 02125, MA, USA.

BMC Bioinformatics
|December 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning model that uses biological network data to predict patient responses to treatments from gene expression data, improving personalized medicine. The method enhances the reproducibility and interpretability of molecular markers for clinical applications.

Keywords:
Artificial neural networkClinical trialGene regulatory networksGroup LassoPrediction of response

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Translational Medicine

Background:

  • Personalized therapies require stratifying patients based on treatment response or adverse reactions.
  • Omic-scale biological measurements offer potential for machine learning in disease diagnosis and progression.
  • Human genetic variability challenges the reproducibility of omic-scale markers.

Purpose of the Study:

  • To develop a robust and reproducible method for predicting clinical phenotypes from transcriptomic data.
  • To improve the accuracy and generalizability of molecular marker identification for personalized medicine.

Main Methods:

  • Developed a biological network-based regularized artificial neural network model.
  • Incorporated regularization for simultaneous shrinkage of gene sets based on upstream regulatory mechanisms.
  • Integrated prior biological knowledge on gene-regulatory interactions.

Main Results:

  • The method accurately predicted clinical outcomes in kidney transplantation and ulcerative colitis trials.
  • Integration of biological knowledge significantly improved prediction robustness and generalizability.
  • The approach demonstrated improved interpretability of biological signatures and stable performance across datasets.

Conclusions:

  • Presents a method for predicting clinical phenotypes from genome-wide expression data using gene-regulatory interactions.
  • Enhances robustness and reproducibility of omic-scale markers for personalized medicine.
  • Group-wise regularization increases interpretability and provides stable performance estimates.