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ICCABS 2023

Dr. Nita Parekh and her students presented the following papers online at the 12th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2023) from 11 to 13 December at University of Oklahoma, Norman, Oklahoma, United States of America:

  • DNA Methylation-Based Subtype Classification of Breast Cancer – Sri Lakshmi Bhavani, Pagolu, Suba S and Nita, Parekh.

Here is the summary of the research work as explained by the authors:

Aberrant genome-wide DNA methylation patterns is common in cancers. Understanding how these affect the transcriptome can provide insights into subtype specific development and progression of tumorigenesis. In this study we carried out genome-wide analysis of DNA methylation and gene expression profiles in TCGA-BRCA breast cancer samples to propose a novel set of 35 methylation-based prognostic markers that may provide insights to molecular subtype specific disease stratification. Gene-set enrichment and pathway analysis of the predicted markers using MSigDB and DAVID revealed their role in mammary gland development pathway, various signaling pathways (ERBB2, NOTCH, etc.), and other cancer pathways, and show clear association with genes affected by hormone receptor status. We further show the discriminative power of the proposed DNA methylation signature in classifying breast cancer samples into three molecular subtypes, viz., Luminal, HER2-enriched and Triple Negative. An accuracy of 94.12% and MCC of 0.87 is obtained in stratified 5-fold cross-validation for the three-class classification using SVM-RBF.

Keywords: DNA Methylation, Breast Cancer, Machine Learning, Subtype Classification.

 

  • Lightweight and Generalizable Model for COVID-19 Detection Using Chest Xray Images – Suba S and Nita, Parekh. 

Here is the summary of the research work as explained by the authors:

Deep learning (DL) has revolutionised the field of medical imaging, including chest radiology, by offering advanced tools for accurate and efficient detection of diseases for over a decade now. Analysis of Chest radiology imag-es (chest X-ray – CXR and Computed tomography – CT) using DL models has widened its scope as a triaging tool since COVID-19 pandemic due to its speed, accuracy, and objectivity of disease detection, leading to better patient outcomes and more efficient healthcare delivery. CNNs are particularly well suited for image analysis tasks due to their ability to capture hierarchical features. Earlier work on Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) have also shown their applicability in the diagnosis of pulmonary diseases. This has led to much recent attention on the analysis of chest radiographs (CXR) using deep learning architectures for the detection of COVID-19 in a clinical setting. Applications developed for medical image analysis require high sensitivity, precision and generalizability along with reliability so as to provide radiologists and clinicians with an additional layer of information to aid in diagnosis. In this work we propose pixel-based attention mechanisms into a lightweight CNN model (Attn-CNN) trained on one of the largest publicly available COVIDx CXR-3 dataset. With much fewer training parameters, it is seen to perform better than four state-of-the-art (SOTA) deep learning models. The generalizability of the model is shown by performing analysis on external dataset. With portable chest radiography (CXR) being commonly used for early disease detection and follow up of lung abnormalities, there is a clear scope of the proposed model in assisting health experts in triaging of patients in pandemic-like situations. Data and code are available at: https://github.com/aleesuss/c19.