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BLASSO: integration of biological knowledge into a regularized linear model.

Daniel Urda1, Francisco Aragón2, Rocío Bautista3

  • 1Universidad de Cádiz, Departamento de Ingeniería Informática, Avda. de la Universidad de Cádiz n°10, Puerto Real, Cádiz, 11519, Spain. daniel.urda@uca.es.

BMC Systems Biology
|November 22, 2018
PubMed
Summary
This summary is machine-generated.

BLASSO, a new model integrating biological knowledge, improves breast cancer outcome prediction and biomarker stability over standard LASSO. It identifies novel cancer-related genes, enhancing RNA-Seq analysis for complex traits.

Keywords:
Biological knowledgeBiomarkers selectionMachine learningPrecision medicineRNA-Seq

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Area of Science:

  • Computational biology and bioinformatics
  • Genomics and transcriptomics
  • Biomedical data analysis

Background:

  • Identifying genetic signatures for complex human traits from RNA-Seq data is challenging due to high gene correlation and instability in biomarker selection.
  • Existing methods often yield low overlap rates for genetic signatures across different studies.
  • Predictive models for complex traits require robust and stable biomarker identification.

Purpose of the Study:

  • To propose BLASSO, a linear model with L1-regularization, incorporating prior biological knowledge for predicting breast cancer outcomes.
  • To evaluate two distinct approaches (Gene-specific and Gene-disease) for integrating biological knowledge within BLASSO.
  • To assess the predictive performance and biomarker stability of BLASSO compared to a baseline LASSO model using RNA-Seq data.

Main Methods:

  • Development of BLASSO, a linear model with L1-regularization, enhanced with two strategies for biological knowledge integration.
  • Utilized a public RNA-Seq gene expression dataset for breast cancer.
  • Employed 10-fold cross-validation with 100 repetitions for model assessment and calculated the robustness index (RI) for biomarker stability.

Main Results:

  • BLASSO achieved higher average AUC values (0.7 for Gene-specific, 0.69 for Gene-disease) compared to LASSO (0.65).
  • BLASSO demonstrated superior biomarker stability, with the Gene-specific approach showing a 66% increase in robustness (RI of 0.15±0.03 vs. 0.09±0.03 for LASSO).
  • Functional analysis of BLASSO-derived signatures revealed known cancer-related genes and identified novel candidates like IFNK and PCNAP1.

Conclusions:

  • BLASSO is an effective choice for breast cancer outcome prediction, offering improved efficacy and biomarker stability over traditional methods.
  • The integration of biological knowledge into BLASSO enhances the interpretability and robustness of identified genetic signatures.
  • BLASSO facilitates the discovery of both established and potentially novel genes involved in cancer pathogenesis.