MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning

Liu, Jin; Zeng, Dejiao; Guo, Rui; Lu, Mingming; Wu, Fang‐Xiang; Wang, Jianxin

doi:10.1007/s10586-020-03199-8

Cited by 25 publications

(6 citation statements)

References 43 publications

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“…Moreover, the NECMA model cannot predict new circRNA-miRNA without any known association. erefore, we will integrate more biological data of circRNA and miRNA in the future, which will make it more reliable [61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Prediction of circRNA‐miRNA Associations Based on Network Embedding

et al. 2021

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circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

Prediction of circRNA‐miRNA Associations Based on Network Embedding

et al. 2021

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“…It is expected that biological information (such as labeled gene sets) will be used in the future to select genes related to cell types in scRNA-seq for further study. Incorporating information from different views may be helpful in improving gene selection (Liu et al, 2020a;Liu et al, 2020b;Lan et al, 2020). There are some differences among the results for scRNA-seq data based on different gene selection methods.…”

Section: Discussionmentioning

confidence: 99%

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

et al. 2021

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In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

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“…e most important feature in loanword identification task is the pronunciation similarity between the word in receipt language and its corresponding word in donor language. As convolutional neural networks (CNNs) have been proven to capture the character-level information in NLP tasks, CNNs can process the sequences in the current receptive filed akin to the attention mechanism [22]. Meanwhile, we also use max pooling to capture character-level features.…”

Section: Featuresmentioning

confidence: 99%

Improving Loanword Identification in Low‐Resource Language with Data Augmentation and Multiple Feature Fusion

Zhu

Nie

2021

Computational Intelligence and Neuroscience

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Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross-lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high-resource languages (such as Chinese, English, and Russian); for low-resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint-based data augmentation method to generate training data for low-resource language loanword identification; then, a loanword identification model based on a log-linear RNN is introduced to improve the performance of low-resource loanword identification by incorporating features such as word-level embeddings, character-level embeddings, pronunciation similarity, and part-of-speech (POS) into one model. Experimental results on loanword identification in Uyghur (in this study, we mainly focus on Arabic, Chinese, Russian, and Turkish loanwords in Uyghur) showed that our proposed method achieves best performance compared with several strong baseline systems.

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MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning

Cited by 25 publications

References 43 publications

Prediction of circRNA‐miRNA Associations Based on Network Embedding

Prediction of circRNA‐miRNA Associations Based on Network Embedding

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

Improving Loanword Identification in Low‐Resource Language with Data Augmentation and Multiple Feature Fusion

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