Scientific Publications
An investigation of cancer cell line-based drug response prediction methods on patient data
2020 Dec 16 KSE 2020
Giang Nguyen, Hoang Le, Diep Quynh Nguyen, Tung Nguyen, Hien Dang, Le Duc-Hau
Tyrosine is mainly degraded in the liver by a series of enzymatic reactions. Abnormal expression of the tyrosine catabolic enzyme tyrosine aminotransferase (TAT) has been reported in patients with hepatocellular carcinoma (HCC). Despite this, aberration in tyrosine metabolism has not been investigated in cancer development. In this work, we conduct comprehensive cross-platform study to obtain foundation for discoveries of potential therapeutics and preventative biomarkers of HCC. We explore data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine and Kaplan Meier plotter (KM plotter) and performed integrated analyses to evaluate the clinical...
Read full articleClinical evaluation of RB1 genetic testing reveals novel mutations in Vietnamese patients with retinoblastoma
2021 Sep 1 MOLECULAR AND CLINICAL ONCOLOGY, 2021
Chinh Quoc Hoang, Hong-Quan Duong, Nguyen Thanh Nguyen, Sy Anh Hao Nguyen, Cuong Nguyen, Bo Duy Nguyen, Lan Tuyet Phung, Dung Thuy Nguyen, Chau Thi Minh Pham, Trang Le Doan, Mai Hoang Tran
Clinical evaluation of the genetic testing strategy is essential for ensuring the correct determination of mutation carriers. The current study retrospectively analyzed genetic and clinicopathological data from 62 Vietnamese patients with retinoblastoma (RB) referred to the Vinmec Hi-Tech Center for RB transcriptional corepressor 1 (RB1) genetic testing between 2017 and 2019. The present study aimed to evaluate the sensitivity of the Next Generation Sequencing (NGS) method to identify novel RB1 mutations, and to consider using age at diagnosis as a risk factor. Genomic DNA was analyzed with custom panel based targeted NGS. NGS was performed on the Beijing Genomics Institute (BGI) sequencing...
Read full articleBRCA1/2 Mutations in Vietnamese Patients with Hereditary Breast and Ovarian Cancer Syndrome
2022 Jan 29 GENES
T.-N.N. Le, V.-K. Tran, T.-T. Nguyen, N.S. Vo, T.H. Hoang, H.-L. Vo, T.-H.T. Nguyen, P.-D. Nguyen, V.-T. Nguyen, T.-V. Ta, H.-T. Tran
(1) Background: Individuals with BRCA1/2 gene mutations are at increased risk of breast and ovarian cancer. The prevalence of BRCA1/2 mutations varies by race and ethnicity, and the prevalence and the risks associated with most BRCA1/2 mutations has not been unknown in the Vietnamese population. We herein screen the entire BRCA1 and BRCA2 genes for breast and ovarian cancer patients with a family history of breast cancer and ovarian cancer, thereby, suggesting a risk score associated with carrier status and history for aiding personalized treatment; (2) Methods: Between December 2017 and December 2019, Vietnamese patients who had a pathological diagnosis of breast and epithelial ovarian cancer were followed up, prospectively,...
Read full articleRIA: a Novel Regression-based Imputation Approach for Single-Cell RNA Sequencing
2019 Dec 5 KSE 2019
Bang Tran, Duc Tran, Hung Nguyen, Nam Vo, Tin Nguyen
Recent technological advancements and availability of genetic databases have facilitated the integration of genetic factors into risk prediction models. A Polygenic Risk Score (PRS) combines the effect of many Single Nucleotide Polymorphisms (SNP) into a single score. This score has lately been shown to have a clinically predictive value in various common diseases. Some clinical interpretations of PRS are summarized in this review for coronary artery disease, breast cancer, prostate cancer, diabetes mellitus, and Alzheimer’s disease. While these findings gave support to the implementation of PRS in clinical settings, the populations of interest were derived mainly from European ancestry. Therefore,...
Read full articleDevelopment and Implementation of Polygenic Risk Score in Vietnamese Population
2019 Dec 31 JOURNAL OF RESEARCH AND DEVELOPMENT ON INFORMATION AND COMMUNICATION TECHNOLOGY, 2020
The-Hung Tran Nguyen, Duc-Hau Le
Recent technological advancements and availability of genetic databases have facilitated the integration of genetic factors into risk prediction models. A Polygenic Risk Score (PRS) combines the effect of many Single Nucleotide Polymorphisms (SNP) into a single score. This score has lately been shown to have a clinically predictive value in various common diseases. Some clinical interpretations of PRS are summarized in this review for coronary artery disease, breast cancer, prostate cancer, diabetes mellitus, and Alzheimer’s disease. While these findings gave support to the implementation of PRS in clinical settings, the populations of interest were derived mainly from European ancestry. Therefore,...
Read full articleUFO: a tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization
2020 Jul 9 PLOS ONE, 2020
Duc-Hau Le
Background Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be...
Read full articleSimilarity calculation, enrichment analysis, and ontology visualization of biomedical ontologies using UFO
2021 Apr 1 CURRENT PROTOCOLS
Quang-Huy Nguyen, Duc-Hau Le
The rapid growth of biomedical ontologies observed in recent years has been reported to be useful in various applications. In this article, we propose two main-function protocols-term-related and entity-related-with the three most common ontology analyses, including similarity calculation, enrichment analysis, and ontology visualization, which can be done by separate methods. Many previously developed tools implementing those methods run on different platforms and implement a limited number of the methods for similarity calculation and enrichment analysis tools for a specific type of biomedical ontology, although any type can be acceptable. Moreover, depending on each application, methods have distinct advantages; thus, the...
Read full articleIntegrating molecular graph data of drugs and multiple -omics data of cell lines for drug response prediction
2021 Jul 14 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Giang T.T. Nguyen, Hoa D. Vu, Duc-Hau Le
Previous studies have either learned drug's features from their string or numeric representations, which are not natural forms of drugs, or only used genomic data of cell lines for the drug response prediction problem. Here, we proposed a deep learning model, GraOmicDRP, to learn drug's features from their graph representation and integrate multiple -omic data of cell lines. In GraOmicDRP, drugs are represented as graphs of bindings among atoms; meanwhile, cell lines are depicted by not only genomic but also transcriptomic and epigenomic data. Graph convolutional and convolutional neural networks were used to learn the representation of drugs and cell...
Read full articleGraph convolutional networks for drug response prediction
2022 Feb 3 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Tuan Nguyen, Giang T.T. Nguyen, Thin Nguyen, Duc-Hau Le
Background: Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent the drugs as strings, which are not a natural way to depict molecules. Also, interpretation (e.g., what are the mutation or copy number aberration contributing to the drug response) has not been considered thoroughly. Methods: In this study, we propose a novel method, GraphDRP, based on graph convolutional network for the problem. In GraphDRP, drugs were represented in molecular graphs directly capturing the bonds among atoms, meanwhile cell lines were...
Read full articlePlant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery
2019 Dec 24 JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2020
Nguyen-Vo, Thanh-Hoang, Nguyen, Loc, Do, Nguyet, Nguyen, Thien-Ngan, Trinh, Khang, Cao, Hung, Le Ly
Nationwide dental health surveys are crucial for providing essential information on dental health and dental condition-related problems in the community. However, the relationship between periodontal conditions and sociodemographic data has not been well investigated in Vietnam. With data from the National Oral Health Survey in 2019, we performed several machine learning methods on this dataset to investigate the impacts of sociodemographic features on gingival bleeding, periodontal pockets, and Community Periodontal Index. From the experiments, LightGBM produced a maximum AUC (area under the curve) value of 0.744. The other models in descending order were logistic regression (0.705), logiboost (0.704), and random...
Read full articleBạn đã sẵn sàng khởi đầu hành trình sức khoẻ từ gen cùng GeneStory?
Đăng ký nhận tư vấn miễn phí
