Exploring Neural Networks and Related Visualization Techniques in Gene Expression Data

Roni Wilentzik Müller*, Irit Gat-Viks

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Over the past decade, neural networks have become one of the cutting-edge methods in various research fields, outshining specifically in complex classification problems. In this paper, we propose two main contributions: first, we conduct a methodological study of neural network modeling for classifying biological traits based on structured gene expression data. Then, we suggest an innovative approach for utilizing deep learning visualization techniques in order to reveal the specific genes important for the correct classification of each trait within the trained models. Our data suggests that this approach have great potential for becoming a standard feature importance tool used in complex medical research problems, and that it can further be generalized to various structured data classification problems outside the biological domain.

Original languageEnglish
Article number402
JournalFrontiers in Genetics
Volume11
DOIs
StatePublished - 15 May 2020

Keywords

  • activation maximization
  • biological traits
  • deep learning
  • gene expression
  • multiclass classification
  • neural networks
  • saliency maps
  • structured data

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