| 1. |
- Tran, Ngoc Hieu, et al.
(author)
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Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data
- 2020
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Journal article (peer-reviewed)abstract
- The under-representation of several ethnic groups in existing genetic databases and studies have undermined our understanding of the genetic variations and associated traits or diseases in many populations. Cost and technology limitations remain the challenges in performing large-scale genome sequencing projects in many developing countries, including Vietnam. As one of the most rapidly adopted genetic tests, non-invasive prenatal testing (NIPT) data offers an alternative untapped resource for genetic studies. Here we performed a large-scale genomic analysis of 2683 pregnant Vietnamese women using their NIPT data and identified a comprehensive set of 8,054,515 single-nucleotide polymorphisms, among which 8.2% were new to the Vietnamese population. Our study also revealed 24,487 disease-associated genetic variants and their allele frequency distribution, especially 5 pathogenic variants for prevalent genetic disorders in Vietnam. We also observed major discrepancies in the allele frequency distribution of disease-associated genetic variants between the Vietnamese and other populations, thus highlighting a need for genome-wide association studies dedicated to the Vietnamese population. The resulted database of Vietnamese genetic variants, their allele frequency distribution, and their associated diseases presents a valuable resource for future genetic studies.
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| 2. |
- Thi Thanh Ngo, Huong, et al.
(author)
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Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam
- 2023
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In: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 135:3, s. 2219-2241
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Journal article (peer-reviewed)abstract
- Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were used for the development of flash flood susceptibility maps for hilly road section (115 km length) of National Highway (NH)-6 in Hoa Binh province, Vietnam. In the proposed models, 88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors. The performance of the models was evaluated using standard statistical measures including Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC) and Root Mean Square Error (RMSE). The results revealed that all the models performed well (AUC > 0.80) in predicting flash flood susceptibility zones, but the performance of the DL model is the best (AUC: 0.972, RMSE: 0.352). Therefore, the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.
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