Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions
This study highlights the potential of utilizing CNN-based approaches to optimize coverage data analysis and improve copy number data normalization. Subsequent CNV detection based on these predicted coordinates facilitates more accurate measurement of coverage profiles and better normalization for GC bias. As a result, this approach could reduce systemic bias and improve the sensitivity and specificity of CNV detection in genomic studies.
Authors
- József Dombi (ORCID: https://orcid.org/0000-0003-3944-6096)
- Peter Juma Ochieng (ORCID: https://orcid.org/0000-0001-6497-6481)
- Miklós Krész (ORCID: https://orcid.org/0000-0002-7547-1128)
- Zoltán Maróti
- Tibor Kalmár
Institutions
- University of Primorska (SI)
- University of Szeged (HU)
- MTA-SZTE Research Group on Artificial Intelligence (HU)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1186/s12859-024-06006-y
- Primary Topic
- Genomic variations and chromosomal abnormalities
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Austrian Science Fund
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
- Szegedi Tudományegyetem
- Nemzeti Kutatási, Fejlesztési és Innovaciós Alap