Ngan Dong

Ngan Dong
Leibniz Universität Hannover · L3S Research Center

About

12
Publications
782
Reads
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29
Citations
Additional affiliations
May 2019 - present
Leibniz Universität Hannover
Position
  • PhD Student
Education
August 2011 - May 2013
Washington State University
Field of study
  • Computer Science

Publications

Publications (12)
Article
Recent studies suggest that miRNA could serve as biomarkers in various human diseases. Since wet-lab experiments are expensive and time-consuming, computational techniques for miRNA-disease association prediction have attracted much attention in recent years. Data scarcity is one of the major challenges in building reliable machine learning models....
Preprint
Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcom...
Article
Full-text available
Background Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. How...
Preprint
Full-text available
Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in the prevention and treatment of virus-related diseases. However, t...
Preprint
Full-text available
Growing evidence from recent studies implies that microRNA or miRNA could serve as biomarkers in various complex human diseases. Since wet-lab experiments are expensive and time-consuming, computational techniques for miRNA-disease association prediction have attracted a lot of attention in recent years. Data scarcity is one of the major challenges...
Preprint
Full-text available
A bstract Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection. This could further help in developing treatments of viral diseases. The main issues in tackling it as a machine learning problem is the scarcity of training data as well input...
Conference Paper
MicroRNA or miRNA is a class of non-coding RNA with a length of approximately 22 nucleotides that is involved in the regulation of gene expression. miRNA is becoming one of the promising drug targets in recent years. Identifying potential associations between miRNA and disease would help in clinical diagnosis, treatment, and drug development. Since...
Conference Paper
The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection(FS) methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal set of the most predictive fea...
Preprint
The identification of biomarkers or predictive features that are indicative of a specific biological or disease state is a major research topic in biomedical applications. Several feature selection methods ranging from simple univariate methods to recent deep-learning methods have been proposed to select a minimal set of the most predictive feature...
Preprint
Full-text available
We propose an attentive neural network for the task of named entity recognition in Vietnamese. The proposed attentive neural model makes use of character-based language models and word embeddings to encode words as vector representations. A neural network architecture of encoder, attention, and decoder layers is then utilized to encode knowledge of...
Article
The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF...

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