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Temporal convolutional network for a Fast DNA mutation detection in breast cancer data.

Untari Novia Wisesty1,2, Tati Rajab Mengko3, Ayu Purwarianti3,4

  • 1Bandung Institute of Technology, Doctoral Program of Electrical Engineering and Informatics, School of Electrical and Information Engineering, Bandung, Indonesia.

Plos One
|May 25, 2023
PubMed
Summary

This study introduces a Temporal Convolutional Network (TCN) for rapid breast cancer mutation detection directly from DNA sequences. The TCN model identifies mutation types and locations without needing reference sequences, improving early cancer diagnosis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Early breast cancer detection is crucial for effective treatment.
  • Current DNA mutation detection methods (alignment, machine learning) have limitations, including reliance on reference sequences and inability to predict mutation indices.
  • There is a need for faster, more accurate, and self-sufficient mutation detection tools.

Purpose of the Study:

  • To propose a novel Temporal Convolutional Network (TCN) model for detecting both the type and index of mutations in DNA sequences.
  • To develop a method for early breast cancer detection that does not require reference sequences or supporting tools.
  • To improve the speed and accuracy of DNA mutation detection.

Main Methods:

  • Developed a Temporal Convolutional Network (TCN) model specifically designed for sequential labeling of DNA data.
  • Implemented 2-mers and 3-mers mapping techniques to enhance mutation detection performance.
  • Evaluated the TCN model on COSMIC and RSCM datasets for mutation type and index detection.

Main Results:

  • The TCN model achieved high F1-scores: 0.9443 on the COSMIC dataset and 0.9629 on the RSCM dataset.
  • The proposed TCN model demonstrated a six-fold increase in speed for index mutation detection compared to the BiLSTM model.
  • The model successfully detected mutation type and index directly from patient DNA sequences without external references or tools.

Conclusions:

  • The TCN model offers a faster and more efficient approach for detecting DNA mutations relevant to early breast cancer diagnosis.
  • This novel method eliminates the need for reference sequences and supporting tools, simplifying the diagnostic process.
  • The TCN model shows significant potential for advancing genomic-based cancer detection technologies.