VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T12:53:53ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2020-10-09T07:11:16Z2020-10-09T07:11:16Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4077This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/40772020-10-09T07:11:16ZOn Rectifying the Mapping between Articles and Institutions in Bibliometric DatabasesToday, bibliometric databases are indispensable sources for researchers and research institutions. The main role of these databases is to find research articles and estimate the performance of researchers and institutions. Regarding the evaluation of the research performance of an organization, the accuracy in determining institutions of authors of articles is decisive. However, current popular bibliometric databases such as Scopus and Web of Science have not addressed this point efficiently. To this end, we propose an approach to revise the authors’ affiliation information of articles in bibliometric databases. We build a model to classify articles to institutions with high accuracy by assembling the bag of words and n-grams techniques for extracting features of affiliation strings. After that, these features are weighted to determine their importance to each institution. Affiliation strings of articles are transformed into the new feature space by integrating weights of features and local characteristics of words and phrases contributing to the sequences. Finally, on the feature space, the support vector classifier method is applied to learn a predictive model. Our experimental result shows that the proposed model’s accuracy is about 99.1%.Kien Tuan NgoDinh Hieu Vohieuvd@vnu.edu.vnNgoc Thang Buithangbn@vnu.edu.vnLe Viet Anh PhamKhanh Ly PhamHai Phan2017-12-08T04:56:21Z2017-12-08T04:56:21Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2688This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/26882017-12-08T04:56:21ZGenomedics: Whole exome analysis system for clinical studiesWhole exome sequencing (WES) is a widely used technique in both medical studies and clinical practice. However, a number of studies show that the results produced by different WES analysis pipelines are not always homogeneous. To this end, we propose a method (called Genomedics) using a consensus approach to expand the list of variants by combining results called from six separate pipelines with sensitive options. To evaluate the performance of the proposed method, Gemomedics was compared to seven existing methods when they were tested on two datasets and F1-score was used as an indicator of accuracy. The results showed that Genomedics has the highest score among seven methods. We also applied Genomedics to analyze whole exomes from Multiple Myeloma and Dravet syndrome patients and found interesting results. The results demonstrate the promising applications of Genomedics in clinical studies.Sy Vinh Levinhls@vnu.edu.vnDuc Canh Nguyencanhnd.uet@gmail.comNgoc Thang Buithangbn@vnu.edu.vnThi Thu Hang DoQuoc Chinh DuongCong Hoang TranBa Hong Minh LeThi Dieu Linh Pham2016-06-02T03:35:55Z2016-06-02T03:35:55Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1632This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/16322016-06-02T03:35:55ZMVRM: A Hybrid Approach to Predict siRNA EfficacyThe discovery of RNA interference (RNAi) leads to design novel drugs for different diseases. Selecting short interfering RNAs (siRNAs) that can knockdown target genes efficiently is one of the key tasks in studying RNAi. A number of predictive models have been proposed to predict knockdown efficacy of siRNAs, however, their performance is still far from the expectation. This work aims to develop a predictive model to enhance siRNA knockdown efficacy prediction. The key idea is to combine both the rule -- based and the model -- based approaches. To this end, views of siRNAs that integrate available siRNA design rules are first learned using an adaptive Fuzzy C Means (FCM) algorithm. The learned views and other properties of siRNAs are combined to final representations of siRNAs. The elastic net regression method is employed to learn a predictive model from these final representations. Experiments on benchmark datasets showed that the proposed method achieved stable and accurate results in comparison with other methods.Ngoc Thang Buithangbn@vnu.edu.vnSy Vinh Levinhls@vnu.edu.vnTu Bao Ho2016-01-06T06:57:59Z2016-03-21T15:35:25Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1468This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/14682016-01-06T06:57:59ZSemi-supervised Tensor Regression Model for siRNA Efficacy PredictionShort interfering RNAs (siRNAs) can knockdown target genes and thus have an immense impact on biology and pharmacy research. The key question of which siRNAs have high knockdown ability in siRNA research remains challenging as current known results are still far from expectation.
This work aims to develop a generic framework to enhance siRNA knockdown efficacy prediction. The key idea is first to enrich siRNA sequences by incorporating them with rules found for designing effective siRNAs and representing them as enriched matrices, then to employ the bilinear tensor regression to predict knockdown efficacy of those matrices. Experiments show that the proposed method achieves better results than existing models in most cases.
Our model not only provides a suitable siRNA representation but also can predict siRNA efficacy more accurate and stable than most of state–of–the–art models. Source codes are freely available on the web at: http://www.jaist.ac.jp/\~bao/BiLTR/Ngoc Thang Buithangbn@vnu.edu.vnTu Bao HoT.A. Kanda2015-06-03T04:38:12Z2015-06-03T04:42:41Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1190This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/11902015-06-03T04:38:12ZA Novel Framework to Improve siRNA Efficacy PredictionShort interfering RNA sequences (siRNAs) can knockdown target genes and thus have an immense impact on biology and pharmacy research. The key question of which siRNAs have high knockdown ability in siRNA research remains challenging as current known results are still far from expectation. This work aims to develop a generic framework to enhance siRNA knockdown efficacy prediction. The key idea is first to enrich siRNA sequences by incorporating them with rules found for designing effective siRNAs and representing them as transformed matrices, then to employ the bilinear tensor regression to do prediction on those matrices. Experiments show that the proposed method achieves results better than existing models in most cases.Ngoc Thang Buithangbn@vnu.edu.vn