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LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

Hongyu Wang1, Zhaomin Yao2, Renli Luo3

  • 1Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Software, Jilin University, Changchun, Jilin 130012, China.

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|February 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces LaCOme, a novel method using convolutional neural networks (CNNs) to analyze inter-feature correlations in omics data. LaCOme features significantly improve classification performance over original transcriptomic data.

Keywords:
Convolutional Neural NetworkFeature EngineeringFeature SelectionLaCOme FeatureTranscriptomic Feature

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Omics analysis involves studying entire genetic or molecular profiles.
  • Transcriptome analysis is crucial for understanding cellular and tissue health.
  • Current computational methods often overlook inter-feature correlations in omics data.

Purpose of the Study:

  • To develop a novel method for extracting inter-feature correlations from transcriptomic data.
  • To enhance the performance of omics data analysis through feature engineering.
  • To improve the classification accuracy of biological data.

Main Methods:

  • Utilized convolutional neural networks (CNNs) to capture inter-feature correlations.
  • Transformed original transcriptomic features into a new space of "LaCOme" features.
  • Employed cross-validation and independent verification to assess feature construction methods.

Main Results:

  • Engineered LaCOme features demonstrated superior classification performance compared to original transcriptomic features across multiple datasets.
  • CNNs can effectively enrich omics data for enhanced computational analysis.
  • Feature construction methods show promise for extracting valuable information from omics data.

Conclusions:

  • The LaCOme approach effectively leverages inter-feature correlations in omics data.
  • CNN-based feature engineering offers a powerful strategy for improving omics data analysis.
  • Novel feature construction methods can enhance the efficiency and effectiveness of omics data interpretation.