MELPMDA: A New Method Based on Matrix Enhancement and Label Propagation for Predicting miRNA-Disease Association

2021 ◽  
pp. 536-548
Author(s):  
Zhen-Wei Zhang ◽  
Zhen Gao ◽  
Chun-Hou Zheng ◽  
Yu-Tian Wang ◽  
Su-Min Qi
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
New Method ◽  
Matrix Enhancement

scholarly journals Graph regularized, semi-supervised learning improves annotation of de novo transcriptomes

2016 ◽  
Author(s):  
Laraib I. Malik ◽  
Shravya Thatipally ◽  
Nikhil Junneti ◽  
Rob Patro
Keyword(s):  
Supervised Learning ◽  
De Novo ◽  
Label Propagation ◽  
New Method ◽  
Differential Analysis ◽  
Graph Topology ◽  
Sequence Relationships

AbstractWe present a new method, GRASS, for improving an initial annotation of de novo transcriptomes. GRASS makes the shared-sequence relationships between assembled contigs explicit in the form of a graph, and applies an algorithm that performs label propagation to transfer annotations between related contigs and modifies the graph topology iteratively. We demonstrate that GRASS increases the completeness and accuracy of the initial annotation, allows for improved differential analysis, and is very efficient, typically taking 10s of minutes.

scholarly journals A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association

2019 ◽  
Vol 20 (7) ◽  
pp. 1549 ◽  
Author(s):  
Yang Liu ◽  
Xiang Feng ◽  
Haochen Zhao ◽  
Zhanwei Xuan ◽  
Lei Wang
Keyword(s):  
Computational Models ◽  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Weighted Matrix ◽  
Disease Associations ◽  
Resource Allocation Strategy ◽  
Simulation Results ◽  
Leave One Out ◽  
Treatment Prognosis

Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well.

scholarly journals ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-Tian Wang ◽  
Lei Li ◽  
Cun-Mei Ji ◽  
Chun-Hou Zheng ◽  
Jian-Cheng Ni
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Biological Information ◽  
Disease Similarity ◽  
Kernel Fusion ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Similarity Networks ◽  
Association Data

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA–disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA–disease associations.

scholarly journals Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

Biomolecules ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 64
Author(s):  
Chen Jin ◽  
Zhuangwei Shi ◽  
Ken Lin ◽  
Han Zhang
Keyword(s):  
Network Architecture ◽  
Matrix Completion ◽  
Disease Association ◽  
Attention Mechanism ◽  
Label Propagation ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Neural Network Architecture ◽  
Disease Associations ◽  
Collaborative Training

Many studies have clarified that microRNAs (miRNAs) are associated with many human diseases. Therefore, it is essential to predict potential miRNA-disease associations for disease pathogenesis and treatment. Numerous machine learning and deep learning approaches have been adopted to this problem. In this paper, we propose a Neural Inductive Matrix completion-based method with Graph Autoencoders (GAE) and Self-Attention mechanism for miRNA-disease associations prediction (NIMGSA). Some of the previous works based on matrix completion ignore the importance of label propagation procedure for inferring miRNA-disease associations, while others cannot integrate matrix completion and label propagation effectively. Varying from previous studies, NIMGSA unifies inductive matrix completion and label propagation via neural network architecture, through the collaborative training of two graph autoencoders. This neural inductive matrix completion-based method is also an implementation of self-attention mechanism for miRNA-disease associations prediction. This end-to-end framework can strengthen the robustness and preciseness of both matrix completion and label propagation. Cross validations indicate that NIMGSA outperforms current miRNA-disease prediction methods. Case studies demonstrate that NIMGSA is competent in detecting potential miRNA-disease associations.

scholarly journals MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation

2018 ◽  
Vol 23 (2) ◽  
pp. 1427-1438 ◽  
Author(s):  
Sheng-Peng Yu ◽  
Cheng Liang ◽  
Qiu Xiao ◽  
Guang-Hui Li ◽  
Ping-Jian Ding ◽  
...  
Keyword(s):  
Matrix Completion ◽  
Disease Association ◽  
Label Propagation ◽  
Novel Method

scholarly journals Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu Chen ◽  
Hongjian Sun ◽  
Mengzhe Sun ◽  
Changguo Shi ◽  
Hongmei Sun ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Colorectal Carcinoma ◽  
Information Integration ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Disease Association ◽  
Biological Data ◽  
Label Propagation ◽  
Close Link ◽  
Stable Performance

Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.

scholarly journals A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction

2019 ◽  
Vol 10 ◽  
Author(s):  
Lei Wang ◽  
Yuqi Wang ◽  
Hao Li ◽  
Xiang Feng ◽  
Dawei Yuan ◽  
...  
Keyword(s):  
Computational Model ◽  
Disease Association ◽  
Label Propagation

Study on Image Retrieval Based on Active Feedback

2013 ◽  
Vol 805-806 ◽  
pp. 1891-1894
Author(s):  
Kun Geng
Keyword(s):  
Image Retrieval ◽  
Positive Feedback ◽  
Image Acquisition ◽  
User Feedback ◽  
Label Propagation ◽  
New Method ◽  
Experimental Result ◽  
Content Based Image Retrieval ◽  
Active Feedback ◽  
Representative Image

Content-based image retrieval, limiting its functionality and semantics. In order to study a new method, this study, with the representative image for more information on user feedback, positive feedback framework proposes two new components called the representative image selection and label propagation. A very large image acquisition experimental result demonstrates the high electiveness positive feedback framework proposal.

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