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Ping Xuan
Heilongjiang University
13Publications
3H-index
34Citations
Publications 13
Newest
#1Ping Xuan (Heilongjiang University)H-Index: 3
#2Yangkun Cao (Heilongjiang University)H-Index: 2
Last.Tonghui Shen (Heilongjiang University)H-Index: 3
view all 6 authors...
4 CitationsSource
Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA–disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these n...
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#1Ping XuanH-Index: 3
#2Nan ShengH-Index: 1
Last.Yahong GuoH-Index: 3
view all 5 authors...
It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source da...
1 CitationsSource
#1Ping XuanH-Index: 3
#2Shuxiang PanH-Index: 1
Last.Hao SunH-Index: 1
view all 5 authors...
Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional netwo...
1 CitationsSource
Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined heterogeneous drug-related and disease-related data to derive the associations between drugs and diseases, while recently developed approaches integrate multiple kinds of drug features, but fail to take the diversity implied by these features into account. We developed a method based on non-negative matrix factorization, DivePred, for predicting pote...
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#1Ping XuanH-Index: 3
#2Hao SunH-Index: 1
Last.Shuxiang PanH-Index: 1
view all 5 authors...
Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network repr...
1 CitationsSource
#1Ping XuanH-Index: 3
#2Yilin YeH-Index: 1
Last.Chang SunH-Index: 1
view all 5 authors...
Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural n...
1 CitationsSource
#1Ping Xuan (Heilongjiang University)H-Index: 3
#2Chang Sun (Heilongjiang University)H-Index: 1
Last.Yihua Dong (Heilongjiang University)H-Index: 2
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Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, ...
3 CitationsSource
#1Ping Xuan (Heilongjiang University)H-Index: 3
#2Yangkun Cao (Heilongjiang University)H-Index: 2
Last.Zhaogong Zhang (Heilongjiang University)H-Index: 1
view all 5 authors...
A great many of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related with human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on the shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermor...
2 CitationsSource
#1Hui Cui (La Trobe University)
#2Tonghui Shen (Heilongjiang University)H-Index: 3
Last.Ping Xuan (Heilongjiang University)H-Index: 3
view all 5 authors...
Identifying new treatments for existing drugs can help reduce drug development costs and explore novel indications of drugs. The prediction of associations between drugs and diseases is challenging because their similarities and relations are complicated and non-linear. We propose a HeteroDualNet model to address this issue. Firstly, three types of matrices are extracted to represent intra-drug similarities, intra-disease similarity and drug-disease associations. The intra-drug similarities cons...
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