Application of deep learning technique in next generation sequence experiments
| dc.authorscopusid | 57199652078 | |
| dc.authorscopusid | 58654602800 | |
| dc.contributor.author | Ozgur, Su | |
| dc.contributor.author | Orman, Mehmet | |
| dc.date.accessioned | 2024-08-25T18:51:57Z | |
| dc.date.available | 2024-08-25T18:51:57Z | |
| dc.date.issued | 2023 | |
| dc.department | Ege Üniversitesi | en_US |
| dc.description.abstract | In recent years, the widespread utilization of biological data processing technology has been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has become an integral component of biological research. NGS technologies enable the sequencing of billions of nucleotides in the entire genome, transcriptome, or specific target regions. This sequencing generates vast data matrices. Consequently, there is a growing demand for deep learning (DL) approaches, which employ multilayer artificial neural networks and systems capable of extracting meaningful information from these extensive data structures. In this study, the aim was to obtain optimized parameters and assess the prediction performance of deep learning and machine learning (ML) algorithms for binary classification in real and simulated whole genome data using a cloud-based system. The ART-simulated data and paired-end NGS (whole genome) data of Ch22, which includes ethnicity information, were evaluated using XGBoost, LightGBM, and DL algorithms. When the learning rate was set to 0.01 and 0.001, and the epoch values were updated to 500, 1000, and 2000 in the deep learning model for the ART simulated dataset, the median accuracy values of the ART models were as follows: 0.6320, 0.6800, and 0.7340 for epoch 0.01; and 0.6920, 0.7220, and 0.8020 for epoch 0.001, respectively. In comparison, the median accuracy values of the XGBoost and LightGBM models were 0.6990 and 0.6250 respectively. When the same process is repeated for Chr 22, the results are as follows: the median accuracy values of the DL models were 0.5290, 0.5420 and 0.5820 for epoch 0.01; and 0.5510, 0.5830 and 0.6040 for epoch 0.001, respectively. Additionally, the median accuracy values of the XGBoost and LightGBM models were 0.5760 and 0.5250, respectively. While the best classification estimates were obtained at 2000 epochs and a learning rate (LR) value of 0.001 for both real and simulated data, the XGBoost algorithm showed higher performance when the epoch value was 500 and the LR was 0.01. When dealing with class imbalance, the DL algorithm yielded similar and high Recall and Precision values. Conclusively, this study serves as a timely resource for genomic scientists, providing guidance on why, when, and how to effectively utilize deep learning/machine learning methods for the analysis of human genomic data. | en_US |
| dc.description.sponsorship | Ege University Office of Scientific Research Projects (BAP) [TDK-2020-21725] | en_US |
| dc.description.sponsorship | This study was supported by Ege University Office of Scientific Research Projects (BAP) (Project ID: TDK-2020-21725). | en_US |
| dc.identifier.doi | 10.1186/s40537-023-00838-w | |
| dc.identifier.issn | 2196-1115 | |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.scopus | 2-s2.0-85174451880 | en_US |
| dc.identifier.scopusquality | Q1 | en_US |
| dc.identifier.uri | https://doi.org/10.1186/s40537-023-00838-w | |
| dc.identifier.uri | https://hdl.handle.net/11454/102782 | |
| dc.identifier.volume | 10 | en_US |
| dc.identifier.wos | WOS:001088044100001 | en_US |
| dc.identifier.wosquality | Q1 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springernature | en_US |
| dc.relation.ispartof | Journal of Big Data | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.snmz | 20240825_G | en_US |
| dc.subject | Next generation sequencing | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Variant calling format | en_US |
| dc.subject | Cloud computing | en_US |
| dc.subject | Convolutional Neural-Networks | en_US |
| dc.subject | Prediction | en_US |
| dc.subject | Identification | en_US |
| dc.subject | Algorithms | en_US |
| dc.subject | Alignment | en_US |
| dc.title | Application of deep learning technique in next generation sequence experiments | en_US |
| dc.type | Article | en_US |
