Cytogenetics and Cytogenomics in Clinical Diagnostics: Innovations and Applications

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Cytogenomics".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 160

Special Issue Editor


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Guest Editor
Department of Pathology and Genomic Medicine, Clinical Cytogenomics, Thomas Jefferson University, Philadelphia, PA 19107, USA
Interests: conventional cytogenetics; cytogenomics; epigenetics; epigenomics; laboratory genetics and genomics

Special Issue Information

Dear Colleagues,

Chromosomes serve as both structural and regulatory units of the human genome. Clinical cytogenetics and cytogenomics enable the diagnosis of constitutional and oncological chromosomal abnormalities. While conventional karyotyping provides genome-wide analysis at low resolution, the integration of fluorescence in situ hybridization (FISH), microarray analysis, and optical genome mapping (OGM) since the 1990s has significantly enhanced diagnostic precision and sensitivity.

Despite advances in genomic sequencing technologies that detect nucleotide-level variants, our understanding of chromosomal alterations at microscopic and submicroscopic levels in human disease remains incomplete. Genome function and phenotypic manifestation are regulated across multiple layers of resolution and nuclear organization, with topological genome features coordinating higher-order gene activity. Conventional karyotyping, FISH, and emerging cytogenomic approaches remain essential for comprehensive genomic assessment, allowing for the detection of not only numerical, structural, and sub-chromosomal abnormalities but also the multiple regulatory layers that govern the genome. Looking ahead, the incorporation of artificial intelligence (AI) holds promise for overcoming the limitations of traditional cytogenetic analysis and is expected to play a transformative role across all genomic medicine subspecialties, including cytogenomics.

This Special Issue invites submissions on chromosomal abnormalities and submicroscopic genomic variations in developmental disorders, neuropsychiatric diseases, and neoplastic conditions. We welcome research on mechanistic insights, genotype–phenotype correlations, technological innovations, and clinical applications. Studies on three-dimensional chromosome organization in human diseases and AI-driven advancements in cytogenomics are particularly encouraged. As the field continues to evolve, we seek perspectives on emerging challenges, future directions, and the transformative potential of next-generation cytogenomic technologies.

Dr. Jinglan Liu
Guest Editor

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Keywords

  • karyotyping
  • cytogenomics
  • chromosome territory (CT)
  • nuclear architecture
  • clinical oncology
  • germline
  • development
  • neuropsychiatry
  • artificial intelligence (AI)

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Published Papers (1 paper)

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Research

12 pages, 1158 KiB  
Article
ChromoCheck: Predicting Postnatal Chromosomal Trisomy Cases Using a Support Vector Machine Learning Model
by Nabras Al-Mahrami, Nuha Al Jabri, Amal A. W. Sallam, Najwa Al Jahdhami and Fahad Zadjali
Genes 2025, 16(6), 695; https://doi.org/10.3390/genes16060695 (registering DOI) - 8 Jun 2025
Abstract
Introduction: Chromosomal study via karyotype is one of the historical gold-standard procedures used to provide a clearer view of chromosomal trisomy abnormalities. It has been used to correlate several phenotypic manifestations that require immediate medical intervention. However, the laboratory procedure persisted with various [...] Read more.
Introduction: Chromosomal study via karyotype is one of the historical gold-standard procedures used to provide a clearer view of chromosomal trisomy abnormalities. It has been used to correlate several phenotypic manifestations that require immediate medical intervention. However, the laboratory procedure persisted with various drawbacks. The recent machine learning model shed light on prediction capabilities in the medical field. In this study, we aimed to use a support vector machine model for predicting postnatal chromosomal trisomy cases. Methods: A dataset of 946 neonatal records from the Royal Hospital, Muscat, Oman, covering the period from 2013 to 2023, has been used in this model. The model is based on features such as thyroxine hormone levels and thyroid-stimulating hormone levels. With different R packages, we used a support vector machine model with leave-one-out cross-validation and ten iterations to test three kernel functions: linear, radial, and polynomial. Results: Among the obtained kernel performances, the linear kernel has optimal classification performance. The training accuracy was 81%, and the testing accuracy was 82%. Sensitivity ranged from 97 to 98%, and specificity ranged from 79 to 80%. The area under the curve in relation to the training dataset came to 0.89, and it came to 0.90 for the test dataset. We deployed the trained models in a website tool called ChromoCheck. Conclusions: Our study is an example of how machine learning can be instrumental in augmenting conventional methods of cytogenetics diagnosis and decision-making in a clinical setup. Full article
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