{"title":"BR-ChromNet: Banding resolution localization of chromosome structural abnormality with conditional random field","authors":"","doi":"10.1016/j.jmb.2024.168733","DOIUrl":null,"url":null,"abstract":"<div><p>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 <span><span>https://github.com/frankchen121212/BR-ChromNet</span><svg><path></path></svg></span></p></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0022283624003425/pdfft?md5=1f8cc93ab57e200eba1557166ba011f2&pid=1-s2.0-S0022283624003425-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022283624003425","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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
期刊介绍:
Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions.
Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.