Học máy y sinh
RIA: 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 more 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 more 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 more 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 more 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 more 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 more article1 to 6 of 6 items
Bạn đã sẵn sàng khởi đầu hành trình sức khoẻ từ gen cùng GeneStory?
Đăng ký nhận tư vấn miễn phí
