التفاصيل البيبلوغرافية
| العنوان: |
Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach. |
| المؤلفون: |
Goh, Chul Jun, Kwon, Hyuk-Jung, Kim, Yoonhee, Jung, Seunghee, Park, Jiwoo, Lee, Isaac Kise, Park, Bo-Ram, Kim, Myeong-Ji, Kim, Min-Jeong, Lee, Min-Seob |
| المصدر: |
Diagnostics (2075-4418); Jan2024, Vol. 14 Issue 1, p84, 16p |
| مصطلحات موضوعية: |
SINGLE nucleotide polymorphisms, JOUBERT syndrome, HUMAN genome, DISEASE susceptibility, MACHINE learning |
| مستخلص: |
Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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