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Pan-Cancer Classification Based on Self-Normalizing Neural Networks and Feature Selection.

Junyi Li1, Qingzhe Xu1, Mingxiao Wu1

  • 1Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.

Frontiers in Bioengineering and Biotechnology
|August 28, 2020
PubMed
Summary

This study introduces a computational method using self-normalizing neural networks (SNN) for pan-cancer classification based on DNA copy number variation. The approach effectively distinguishes cancer types, outperforming random forest methods.

Keywords:
cancer classificationcopy number variationfeature selectionpan-cancerself-normalizing neural network

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Area of Science:

  • Computational biology
  • Genomics
  • Machine learning in oncology

Background:

  • Cancer classification is crucial for diagnosis and treatment.
  • Similar molecular features, like DNA copy number variations, complicate pan-cancer classification.
  • Accurate molecular-level classification remains a significant challenge.

Purpose of the Study:

  • To develop a computational method for classifying cancer types using pan-cancer copy number variation data.
  • To leverage self-normalizing neural networks (SNN) for improved cancer classification accuracy.
  • To assess the efficacy of the proposed method against existing techniques like random forest.

Main Methods:

  • Utilized self-normalizing neural networks (SNN) for analyzing high-dimensional copy number variation data.
  • Employed Monte Carlo feature selection to identify and rank relevant genetic features.
  • Selected 3,694 features for the predictive model.

Main Results:

  • The SNN-based classifier achieved an accuracy of 0.798 and a macro F1 score of 0.789.
  • The proposed method demonstrated superior performance compared to the random forest classifier in distinguishing four cancer types.
  • The results indicate strong predictive power for the developed computational method.

Conclusions:

  • The proposed SNN and feature selection method offers a powerful tool for pan-cancer classification using DNA copy number variation.
  • This computational approach shows promise for improving cancer diagnosis and treatment strategies.
  • The method is extendable to other molecular features for broader pan-cancer analysis.