scholarly journals Network-based collaborative filtering recommendation model for inferring novel disease-related miRNAs

RSC Advances ◽  
2017 ◽  
Vol 7 (71) ◽  
pp. 44961-44971 ◽  
Author(s):  
Changlong Gu ◽  
Bo Liao ◽  
Xiaoying Li ◽  
Lijun Cai ◽  
Haowen Chen ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Disease Association ◽  
Association Network ◽  
Recommendation Algorithm ◽  
Similarity Network ◽  
Disease Similarity

According to the miRNA and disease similarity network, the unknown associations are predicted by combining the known miRNA-disease association network based on collaborative filtering recommendation algorithm.

scholarly journals A network similarity integration method for predicting microRNA-disease associations

RSC Advances ◽  
2017 ◽  
Vol 7 (51) ◽  
pp. 32216-32224 ◽  
Author(s):  
Xiaoying Li ◽  
Yaping Lin ◽  
Changlong Gu
Keyword(s):  
Integration Method ◽  
Disease Association ◽  
Association Network ◽  
Similarity Network ◽  
Novel Mirna ◽  
Disease Similarity ◽  
Disease Associations ◽  
Network Similarity

The NSIM integrates the disease similarity network, miRNA similarity network, and known miRNA-disease association network on the basis of cousin similarity to predict not only novel miRNA-disease associations but also isolated diseases.

scholarly journals Predicting Drug-Disease Association Based on Ensemble Strategy

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianlin Wang ◽  
Wenxiu Wang ◽  
Chaokun Yan ◽  
Junwei Luo ◽  
Ge Zhang
Keyword(s):  
Prediction Models ◽  
Drug Repositioning ◽  
Predictive Ability ◽  
Disease Association ◽  
New Drugs ◽  
Similarity Network ◽  
Disease Similarity ◽  
Ensemble Strategy ◽  
Disease Associations ◽  
Reducing Costs

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

scholarly journals SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Li ◽  
Zhen Gao ◽  
Chun-Hou Zheng ◽  
Yu Wang ◽  
Yu-Tian Wang ◽  
...  
Keyword(s):  
Cross Validation ◽  
Matrix Completion ◽  
Disease Association ◽  
Human Diseases ◽  
Data Types ◽  
Similarity Network ◽  
Multiple Data ◽  
Disease Similarity ◽  
Leave One Out ◽  
Novel Model

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA–disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA–disease.

scholarly journals A learning-based framework for miRNA-disease association identification using neural networks

2019 ◽  
Vol 35 (21) ◽  
pp. 4364-4371 ◽  
Author(s):  
Jiajie Peng ◽  
Weiwei Hui ◽  
Qianqian Li ◽  
Bolin Chen ◽  
Jianye Hao ◽  
...  
Keyword(s):  
Essential Feature ◽  
Interaction Network ◽  
Disease Association ◽  
Feature Representation ◽  
Supplementary Information ◽  
Similarity Network ◽  
Protein Protein Interaction ◽  
Non Coding Rna ◽  
Disease Similarity ◽  
Protein Protein Interaction Network

Abstract Motivation A microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many biological processes. Lots of studies have shown that miRNAs are implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes. Results We propose a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures interaction features between diseases and miRNAs based on a three-layer network including disease similarity network, miRNA similarity network and protein-protein interaction network. Then, it employs an auto-encoder to identify the essential feature combination for each pair of miRNA and disease automatically. Finally, taking the reduced feature representation as input, it uses a convolutional neural network to predict the final label. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on both tasks of miRNA-disease association prediction and miRNA-phenotype association prediction. Availability and implementation The source code and data are available at https://github.com/Issingjessica/MDA-CNN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals M171. THE GENE-SHARING RELATIONSHIP OF SCHIZOPHRENIA WITH OTHER MENTAL OR SYSTEMIC DISORDERS: A DISEASE-SIMILARITY NETWORK ANALYSIS FOCUSED ON EGOCENTRIC NETWORK

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S201-S202
Author(s):  
Seong Hoon Jeong ◽  
Hee-Yeon Jung ◽  
In Won Chung ◽  
Yong Sik Kim
Keyword(s):  
Depressive Disorder ◽  
Psychiatric Illness ◽  
Disease Association ◽  
Genome Wide Association Studies ◽  
Similarity Network ◽  
Disease Similarity ◽  
Study Results ◽  
Wide Range ◽  
Egocentric Network ◽  
Gene Sharing

Abstract Background Schizophrenia is an archetypal example that a psychiatric illness may not merely be a mental or a brain disorder but rather a systemic illness. It can be glimpsed from a wide range of biomarkers that span all the imaginable body systems, and from higher co-morbidity with other systemic illnesses. However, quantitative analysis of schizophrenia’s relationship with other diseases are not yet satisfactory. Genome-wide association studies have identified more than hundreds of genetic loci associated with schizophrenia. In turn, these loci are associated with a wide variety of other diseases. From this gene-disease relationship, a bipartite network can be built which, after appropriate projection, could help to map a complex disease-similarity network. In case of schizophrenia, it would reveal the position of schizophrenia among the broader categories of systemic illnesses. Methods DisGeNET is a discovery platform which contains one of the largest collections of gene-disease association data. The major source of the integrated data is the automatized curation from MEDLINE abstract. Therefore, it contains the timestamp of reported gene-disease association. Gene-disease-timestamp (year of publication) triplet was fed into a Neo4J graph database platform. From this, disease-disease relationships with shared gene count and Jaccard similarity score was extracted. The network structure of level 1.5 egocentric network centered upon schizophrenia was inspected. Louvain community detection algorithm was applied to expose underlying group structure among the 1st order alters. For comparison, similar ego-networks centered upon several major psychiatric illnesses were also inspected. Finally, the yearly variation of Jaccard score which reflected the accumulation of research data were monitored. Results The diseases which showed the highest Jaccard score (j) were bipolar disorder (j=0.203) and depressive disorder (j=0.190) as expected. Other diseases with meaningful similarity could be grouped into three communities: 1) psychiatric illness including bipolar/depressive disorder, 2) a variety of malignancies including neuroblastoma (j=0.083), stomach cancer (j=0.070) and pancreatic cancer (j=0.065) 3) other systemic illnesses including multiple sclerosis (j=0.088), metabolic syndrome (j=0.076), myocardial infarction (j=0.073), rheumatoid arthritis (j=0.070), lupus erythematosus (0.056). The gene-sharing relationship with systemic illnesses (malignancies and other) began to be revealed after 2005. Since then, more and more evidences were accumulated to solidify the schizophrenia’s link with systemic illnesses. Discussion Recently, a couple of large-scale epidemiological studies verified the significant correlation between prevalence of schizophrenia and cancer/autoimmune disorders. The present study results may augment these epidemiological data and thus strongly support the concept of schizophrenia as a systemic illness. Gene-sharing and its reflection in prevalence data would indicate deeper link at the level of pathogenesis with systemic illnesses. Recently, many authors contemplated the possible link between schizophrenia and cancer in terms of cell cycle regulation and control of apoptosis. Likewise, others suspected immunological disturbance as the fundamental mechanism of schizophrenia. In this vein, the need for extending the concept of mental disorders as a focused manifestation of systemic illness seems gaining impetus.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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