A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction

2017 ◽  
Vol 13 (6) ◽  
pp. 1202-1212 ◽  
Author(s):  
Xing Chen ◽  
Zhi-Chao Jiang ◽  
Di Xie ◽  
De-Shuang Huang ◽  
Qi Zhao ◽  
...  
Keyword(s):  
Computational Model ◽  
Semantic Similarity ◽  
Computational Models ◽  
Disease Association ◽  
Functional Similarity ◽  
Model Based ◽  
Disease Associations

Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction (SDMMDA) to predict potential miRNA–disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs.

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

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 PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations

2018 ◽  
Vol 19 (11) ◽  
pp. 3410 ◽  
Author(s):  
Xiujuan Lei ◽  
Zengqiang Fang ◽  
Luonan Chen ◽  
Fang-Xiang Wu
Keyword(s):  
Semantic Similarity ◽  
Heterogeneous Network ◽  
Functional Similarity ◽  
Computational Method ◽  
Related Gene ◽  
Similarity Network ◽  
Disease Pair ◽  
Disease Associations ◽  
Leave One Out ◽  
Similarity Scores

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

scholarly journals MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Yu-Tian Wang ◽  
Qing-Wen Wu ◽  
Zhen Gao ◽  
Jian-Cheng Ni ◽  
Chun-Hou Zheng
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Experimental Studies ◽  
Prediction Method ◽  
Disease Association ◽  
High Dimensionality ◽  
K Nearest Neighbor ◽  
Hypergraph Learning ◽  
Disease Associations

Abstract Background MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time‐consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. Methods This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. Result Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. Conclusion MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA–disease association predication.

ES-MDA: Enhanced Similarity-based MiRNA-Disease Association

2020 ◽  
Vol 21 (11) ◽  
pp. 1060-1067
Author(s):  
Li Xu ◽  
Ge-Ning Jiang
Keyword(s):  
Machine Learning ◽  
Semantic Similarity ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Computational Prediction ◽  
Disease Association ◽  
Biological Resource ◽  
Similarity Network ◽  
Machine Learning Methods ◽  
Disease Associations

: Accumulating evidence demonstrate that miRNAs can be treated as critical biomarkers in various complex human diseases. Thus, the identifications on potential miRNA-disease associations have become a hotpot for providing better understanding of disease pathology in this field. Recently, with various biological datasets, increasingly computational prediction approaches have been designed to uncover disease-related miRNAs for further experimental validation. To improve the prediction accuracy, several algorithms integrated miRNA similarities of known miRNA-disease associations to enhance the miRNA functional similarity network and disease similarities of known miRNA-disease associations to enhance the disease semantic similarity network. It is anticipated that machine learning methods would become an effective biological resource for clinical experimental guidance.

Hierarchical Extension Based on the Boolean Matrix for LncRNA-Disease Association Prediction

2020 ◽  
Vol 20 (6) ◽  
pp. 452-460
Author(s):  
Lin Tang ◽  
Yu Liang ◽  
Xin Jin ◽  
Lin Liu ◽  
Wei Zhou
Keyword(s):  
Computational Models ◽  
Characteristic Curve ◽  
Experimental Studies ◽  
Potential Method ◽  
Disease Association ◽  
Biological Data ◽  
Boolean Matrix ◽  
Disease Associations ◽  
Association Data ◽  
Association Matrix

Background: Accumulating experimental studies demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA–disease associations have been experimentally verified at present. Automatically predicting LncRNA–disease associations based on computational models can save the huge cost of wet-lab experiments. Methods and Result: To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we propose a hierarchical extension based on the Boolean matrix for LncRNA-disease association prediction model (HEBLDA). HEBLDA discovers the intrinsic hierarchical correlation based on the property of the Boolean matrix from various relational sources. Then, HEBLDA integrates these hierarchical associated matrices by fusion weights. Finally, HEBLDA uses the hierarchical associated matrix to reconstruct the LncRNA– disease association matrix by hierarchical extending. HEBLDA is able to work for potential diseases or LncRNA without known association data. In 5-fold cross-validation experiments, HEBLDA obtained an area under the receiver operating characteristic curve (AUC) of 0.8913, improving previous classical methods. Besides, case studies show that HEBLDA can accurately predict candidate disease for several LncRNAs. Conclusion: Based on its ability to discover the more-richer correlated structure of various data sources, we can anticipate that HEBLDA is a potential method that can obtain more comprehensive association prediction in a broad field.

NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion

2020 ◽  
Author(s):  
Xing Chen ◽  
Lian-Gang Sun ◽  
Yan Zhao
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Matrix Completion ◽  
Critical Role ◽  
Disease Association ◽  
Human Diseases ◽  
Superior Performance ◽  
Main Task ◽  
Constraint Matrix ◽  
Disease Associations

Abstract Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse fundamental and important biological processes associated with human diseases. Inferring potential disease related miRNAs and employing them as the biomarkers or drug targets could contribute to the prevention, diagnosis and treatment of complex human diseases. In view of that traditional biological experiments cost much time and resources, computational models would serve as complementary means to uncover potential miRNA–disease associations. In this study, we proposed a new computational model named Neighborhood Constraint Matrix Completion for MiRNA–Disease Association prediction (NCMCMDA) to predict potential miRNA–disease associations. The main task of NCMCMDA was to recover the missing miRNA–disease associations based on the known miRNA–disease associations and integrated disease (miRNA) similarity. In this model, we innovatively integrated neighborhood constraint with matrix completion, which provided a novel idea of utilizing similarity information to assist the prediction. After the recovery task was transformed into an optimization problem, we solved it with a fast iterative shrinkage-thresholding algorithm. As a result, the AUCs of NCMCMDA in global and local leave-one-out cross validation were 0.9086 and 0.8453, respectively. In 5-fold cross validation, NCMCMDA achieved an average AUC of 0.8942 and standard deviation of 0.0015, which demonstrated NCMCMDA’s superior performance than many previous computational methods. Furthermore, NCMCMDA was applied to three different types of case studies to further evaluate its prediction reliability and accuracy. As a result, 84% (colon neoplasms), 98% (esophageal neoplasms) and 98% (breast neoplasms) of the top 50 predicted miRNAs were verified by recent literature.

scholarly journals Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Xing Chen ◽  
Chenggang Clarence Yan ◽  
Cai Luo ◽  
Wen Ji ◽  
Yongdong Zhang ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Functional Similarity ◽  
Similarity Network ◽  
Disease Associations

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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