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Evaluation of classification and forecasting methods on time series gene expression data.

Nafis Irtiza Tripto1, Mohimenul Kabir1, Md Shamsuzzoha Bayzid1

  • 1Department of Computer Science and Engineering, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh.

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|November 6, 2020
PubMed
Summary

This study evaluates machine learning for time series gene expression analysis. Deep learning excels at classification, while supervised methods outperform clustering when labels are available for gene expression forecasting.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Time series gene expression data is crucial for understanding dynamic biological processes.
  • Current analyses often overlook the temporal nature of gene expression data, focusing on clustering rather than forecasting or classification.
  • Techniques from other time series domains (e.g., finance, weather) are underexplored in genomics.

Purpose of the Study:

  • To comprehensively evaluate traditional and deep learning methods for time series gene expression classification and forecasting.
  • To propose novel deep learning approaches for these tasks.
  • To compare the performance of these methods against existing state-of-the-art techniques.

Main Methods:

  • Evaluation of traditional unsupervised and supervised machine learning algorithms.
  • Implementation and assessment of deep learning techniques for time series classification and forecasting.
  • Comparative analysis on five real-world gene expression datasets.

Main Results:

  • Deep learning methods generally demonstrate superior performance for time series gene expression classification compared to traditional approaches.
  • Supervised classification proves more effective than clustering when gene expression labels are provided.
  • For forecasting, autoregressive statistical models excel at short-term predictions, while deep learning methods are better for long-term forecasting.

Conclusions:

  • Deep learning offers significant advantages for classifying time series gene expression data.
  • Leveraging available labels through supervised classification enhances analytical power.
  • The choice of forecasting method (statistical vs. deep learning) depends on the prediction horizon for gene expression data.