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PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data.

Jie Hao1, Youngsoon Kim2, Tae-Kyung Kim3,4

  • 1Kennesaw State University, Kennesaw, USA.

BMC Bioinformatics
|December 19, 2018
PubMed
Summary

A new deep learning model, PASNet, accurately predicts patient prognosis using genomic data and biological pathways. This interpretable model improves upon existing methods for complex diseases like Glioblastoma multiforme.

Keywords:
Glioblastoma multiformeLong-term survival predictionPathway-based analysisPrognosis predictionSparse deep neural networkTCGA

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Area of Science:

  • Genomic Medicine
  • Computational Biology
  • Artificial Intelligence in Healthcare

Background:

  • Predicting patient prognosis from large-scale genomic data is challenging due to biological complexity and high-dimensional, low-sample size data.
  • Current methods often struggle with the intricate hierarchical relationships within biological systems, leading to poor prognostic performance in many diseases.
  • The need for robust computational solutions that can handle complex biological data for accurate prognosis prediction is critical.

Purpose of the Study:

  • To develop a novel deep learning model, the Pathway-Associated Sparse Deep Neural Network (PASNet), for accurate prognosis prediction.
  • To enhance model interpretability by incorporating biological pathways and sparse solutions.
  • To apply PASNet to Glioblastoma multiforme (GBM) for long-term survival prediction and validate its performance.

Main Methods:

  • PASNet models a multilayered, hierarchical biological system of genes and pathways using deep learning.
  • A sparse solution is incorporated to provide model interpretability, a feature lacking in conventional fully-connected neural networks.
  • The model was applied to Glioblastoma multiforme (GBM) for long-term survival prediction and rigorously evaluated using cross-validation experiments.

Main Results:

  • PASNet demonstrated superior predictive performance for long-term survival in GBM compared to previous classifiers, evidenced by higher Area Under the Curve (AUC) and F1-scores.
  • The significance of PASNet's performance was statistically assessed using the Wilcoxon signed-rank test.
  • Biological pathways identified by PASNet were found to be consistent with significant pathways previously reported in GBM research, validating its biological relevance.

Conclusions:

  • PASNet accurately predicts prognosis and elucidates complex biological systems underlying clinical outcomes, outperforming current state-of-the-art methods.
  • As the first pathway-based deep neural network representing hierarchical gene-pathway interactions and nonlinear effects, PASNet offers a promising approach.
  • The model's flexible representation and interpretability highlight the strengths of deep learning in genomic medicine, with open-source code available.