A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

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Discovering latent node Information by graph attention network

Scientific Reports ◽

10.1038/s41598-021-85826-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Weiwei Gu ◽

Fei Gao ◽

Xiaodan Lou ◽

Jiang Zhang

Keyword(s):

Supervised Learning ◽

Link Prediction ◽

Learning Algorithm ◽

Network Visualization ◽

Graph Structure ◽

High Quality ◽

Node Centrality ◽

Influential Nodes ◽

Node Classification ◽

Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

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A model based on random walk with restart to predict circRNA-disease associations on heterogeneous network

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ◽

10.1145/3341161.3343514 ◽

2019 ◽

Author(s):

Hüseyin Vural ◽

Mehmet Kaya ◽

Reda Alhajj

Keyword(s):

Random Walk ◽

Heterogeneous Network ◽

Random Walk With Restart ◽

Model Based ◽

Disease Associations

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Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

Bioinformatics ◽

10.1093/bioinformatics/btq108 ◽

2010 ◽

Vol 26 (9) ◽

pp. 1219-1224 ◽

Cited By ~ 238

Author(s):

Yongjin Li ◽

Jagdish C. Patra

Keyword(s):

Heterogeneous Network ◽

Gene Network ◽

Genetic Diseases ◽

Supplementary Information ◽

Disease Genes ◽

Phenotypic Data ◽

Disease Associations ◽

Improved Performance ◽

Leave One Out ◽

Phenotype Network

Abstract Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene–phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases. Results: In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype–gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene–phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence. Availability: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

Bioinformatics ◽

10.1093/bioinformatics/btab706 ◽

2021 ◽

Author(s):

Yang Xu ◽

Priyojit Das ◽

Rachel Patton McCord

Keyword(s):

Deep Learning ◽

Mutual Information ◽

Single Cell ◽

Learning Algorithm ◽

Cellular Systems ◽

Supplementary Information ◽

Omics Data ◽

Learning Approaches ◽

Rna Seq ◽

Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

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A Hybrid Method Based on Semi-Supervised Learning for Relation Extraction in Chinese EMRs (Preprint)

10.2196/preprints.28220 ◽

2021 ◽

Author(s):

ChunMing Yang

Keyword(s):

Supervised Learning ◽

Learning Algorithm ◽

Medical Knowledge ◽

Relation Extraction ◽

Small Scale ◽

Semantic Features ◽

Training Process ◽

Network Layers ◽

Relation Prediction ◽

The Cost

BACKGROUND Extracting relations between the entities from Chinese electronic medical records(EMRs) is the key to automatically constructing medical knowledge graphs. Due to the less available labeled corpus, most of the current researches are based on shallow networks, which cannot fully capture the complex semantic features in the text of Chinese EMRs. OBJECTIVE In this study, a hybrid deep learning method based on semi-supervised learning is proposed to extract the entity relations from small-scale complex Chinese EMRs. METHODS The semantic features of sentences are extracted by residual network (ResNet) and the long dependent information is captured by bidirectional GRU (Gated Recurrent Unit). Then the attention mechanism is used to assign weights to the extracted features respectively, and the output of the two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process. RESULTS The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN model. The F1-score on DAP, SAP, SNAP, TeRD, TeAP, TeCP, TeRS, TeAS, TrAD, TrRD and TrAP 11 relation categories reaches 80.95%, 93.91%, 92.96%, 88.43%, 86.54%, 85.58%, 87.96%, 94.74%, 93.01%, 87.58% and 95.48%, respectively. CONCLUSIONS The hybrid neural network method strengthens the feature transfer and reuse between different network layers and reduces the cost of manual tagging relations. The results demonstrate that our proposed method is effective for the relation extraction in Chinese EMRs.

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An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Algorithms ◽

10.3390/a11090139 ◽

2018 ◽

Vol 11 (9) ◽

pp. 139 ◽

Cited By ~ 5

Author(s):

Ioannis Livieris ◽

Andreas Kanavos ◽

Vassilis Tampakas ◽

Panagiotis Pintelas

Keyword(s):

Supervised Learning ◽

Predictive Models ◽

Learning Algorithm ◽

Learning Algorithms ◽

Classification Problem ◽

Classification Methods ◽

Training Algorithm ◽

Traditional Classification ◽

Supervised Learning Algorithms ◽

Significant Research

Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

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Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Cells ◽

10.3390/cells8091012 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1012 ◽

Cited By ~ 12

Author(s):

Xuan ◽

Pan ◽

Zhang ◽

Liu ◽

Sun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Heterogeneous Network ◽

Heterogeneous Data ◽

Superior Performance ◽

Convolutional Network ◽

Topological Information ◽

Disease Pair ◽

Disease Associations ◽

The Right

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 network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

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