BR-ChromNet: Banding resolution localization of chromosome structural abnormality with conditional random field.

Autor: Chen S; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia., Hu T; Department of Pediatric Endocrinology and Genetics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute for Pediatric Research, Shanghai, China., Li N; Smiltec (Suzhou) Co., Ltd., Room 401B, Building B6, No. 218 Xinghu Street, Suzhou Industrial Park, Suzhou, Jiangsu, China., Gao X; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia., Yu Y; Department of Pediatric Endocrinology and Genetics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Institute for Pediatric Research, Shanghai, China. Electronic address: yuyongguo@shsmu.edu.cn.
Jazyk: angličtina
Zdroj: Journal of molecular biology [J Mol Biol] 2024 Oct 15; Vol. 436 (20), pp. 168733. Date of Electronic Publication: 2024 Aug 14.
DOI: 10.1016/j.jmb.2024.168733
Abstrakt: Detecting chromosome structural abnormalities in medical genetics is essential for diagnosing genetic disorders and understanding their implications for an individual's health. However, existing computational methods are formulated as a binary-class classification problem trained only on representations of positive/negative chromosome pairs. This paper introduces an innovative framework for detecting chromosome abnormalities with banding resolution, capable of precisely identifying and masking the specific abnormal regions. We highlight a pixel-level abnormal mapping strategy guided by banding features. This approach integrates data from both the original image and banding characteristics, enhancing the interpretability of prediction results for cytogeneticists. Furthermore, we have implemented an ensemble approach that pairs a discriminator with a conditional random field heatmap generator. This combination significantly reduces the false positive rate in abnormality screening. We benchmarked our proposed framework with state-of-the-art (SOTA) methods in abnormal screening and structural abnormal region segmentation. Our results show cutting-edge effectiveness and greatly reduce the high false positive rate. It also shows superior performance in sensitivity and segmentation accuracy. Being able to identify abnormal regions consistently shows that our model has demonstrated significant clinical utility with high model interpretability. BRChromNet is open-sourced and available at https://github.com/frankchen121212/BR-ChromNet.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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