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Methyl-binding DNA capture Sequencing for Patient Tissues

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Published on: October 31, 2016

EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data.

Punam Bedi¹, Surbhi Rani¹, Bhavna Gupta²

¹Department of Computer Science, University of Delhi, Delhi, India.

Computer Methods and Programs in Biomedicine

|December 12, 2024

Summary

A new lightweight model, EpiBrCan-Lite, accurately classifies breast cancer subtypes using DNA methylation data. This approach significantly reduces trainable weight parameters while maintaining high performance, addressing limitations of existing methods.

Keywords:

Breast cancer disease DNA methylation data Epigenomic Gated recurrent unit SMOTE Transformer encoder

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Computational Biology and Bioinformatics
Machine Learning in Oncology
Genomic Data Analysis

Background:

Accurate breast cancer subtype classification is crucial for patient prognosis and survival rates.
Existing Machine Learning and Deep Learning models often suffer from high trainable weight parameters, low performance, and class imbalance issues.
DNA methylation data offers a promising avenue for subtype classification but requires efficient analytical models.

Purpose of the Study:

To develop a lightweight model for breast cancer subtype classification using DNA methylation data.
To address the shortcomings of existing models, specifically large trainable weight parameters and class imbalance.
To improve the efficiency and deployability of breast cancer classification models on resource-constrained devices.

Main Methods:

Proposed EpiBrCan-Lite, a novel lightweight model comprising Data Encoding, TransGRU, and Classification blocks.
The TransGRU block modifies the traditional Transformer Encoder by replacing the MLP module with a GRU module to reduce trainable weight parameters and capture long-range dependencies.
Utilized Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance in the TCGA breast cancer dataset.

Main Results:

EpiBrCan-Lite achieved high performance metrics: 95.85% accuracy, 95.96% recall, 95.85% precision, and 95.90% F1-score.
The model demonstrated significantly reduced trainable weight parameters, using only 1/1500 compared to state-of-the-art models.
Low False Positive Rate (FPR) of 1.03% and False Negative Rate (FNR) of 4.12% were recorded.

Conclusions:

The EpiBrCan-Lite model provides efficient and accurate classification of breast cancer subtypes.
Its lightweight architecture makes it suitable for deployment on devices with limited computational power.
This study offers a viable solution for improving breast cancer prognosis through advanced, resource-efficient machine learning.