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Performance Analysis of Deep Learning Models for Binary Classification of Cancer Gene Expression Data.

Subhasree Majumder1, Yogita1, Vipin Pal1

  • 1Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Meghalaya, India.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in healthcare

Background:

  • Accurate patient classification using gene expression data is crucial for cancer diagnosis.
  • Deep learning models, including multilayer perceptron and convolutional neural networks, show promise for analyzing complex biological datasets.

Purpose of the Study:

  • To comprehensively analyze the performance of deep learning models for cancer classification using gene expression profiles.
  • To evaluate different deep learning architectures and feature selection methods across various cancer types.

Main Methods:

  • Investigated three deep learning models: multilayer perceptron and convolutional neural networks.
  • Incorporated two feature selection methods.
  • Evaluated performance on four distinct cancer gene expression datasets (two balanced, two imbalanced).
  • Analyzed a total of 24 unique model-dataset-feature selection combinations.

Main Results:

  • Deep learning models demonstrated robust performance across multiple metrics, including true positive rate, precision, F1-score, and accuracy.
  • Performance was consistent across both balanced and imbalanced datasets.
  • The study provides a consolidated analysis of deep learning efficacy in cancer gene expression classification.

Conclusions:

  • Deep learning models are highly effective for classifying cancer patients based on gene expression data.
  • The findings support the use of deep learning in bioinformatics for cancer diagnostics.
  • Further research can explore advanced deep learning architectures for improved cancer subtyping.