INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.

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SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood

Journal of Cellular and Molecular Medicine ◽

10.1111/jcmm.13799 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5109-5120 ◽

Cited By ~ 4

Author(s):

Yu Qu ◽

Huaxiang Zhang ◽

Cheng Liang ◽

Pingjian Ding ◽

Jiawei Luo

Keyword(s):

Disease Associations ◽

Novel Method

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Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients

Psychological Medicine ◽

10.1017/s0033291719002034 ◽

2019 ◽

Vol 50 (13) ◽

pp. 2154-2170 ◽

Cited By ~ 7

Author(s):

Amit Lazarov ◽

Benjamin Suarez-Jimenez ◽

Ofir Levi ◽

Daniel D. L. Coppersmith ◽

Gadi Lubin ◽

...

Keyword(s):

Depressive Symptoms ◽

Network Topology ◽

Ptsd Symptom ◽

Self Report ◽

Small Samples ◽

Treatment Seeking ◽

Psychological Reactions ◽

Posttraumatic Symptoms ◽

Novel Method ◽

Robust Reliability

AbstractBackgroundDespite extensive research, symptom structure of posttraumatic stress disorder (PTSD) is highly debated. The network approach to psychopathology offers a novel method for understanding and conceptualizing PTSD. However, extant studies have mainly used small samples and self-report measures among sub-clinical populations, while also overlooking co-morbid depressive symptoms.MethodsPTSD symptom network topology was estimated in a sample of 1489 treatment-seeking veteran patients based on a clinician-rated PTSD measure. Next, clinician-rated depressive symptoms were incorporated into the network to assess their influence on PTSD network structure. The PTSD-symptom network was then contrasted with the network of 306 trauma-exposed (TE) treatment-seeking patients not meeting full criteria for PTSD to assess corresponding network differences. Finally, a directed acyclic graph (DAG) was computed to estimate potential directionality among symptoms, including depressive symptoms and daily functioning.ResultsThe PTSD symptom network evidenced robust reliability. Flashbacks and getting emotionally upset by trauma reminders emerged as the most central nodes in the PTSD network, regardless of the inclusion of depressive symptoms. Distinct clustering emerged for PTSD and depressive symptoms within the comorbidity network. DAG analysis suggested a key triggering role for re-experiencing symptoms. Network topology in the PTSD sample was significantly distinct from that of the TE sample.ConclusionsFlashbacks and psychological reactions to trauma reminders, along with their strong connections to other re-experiencing symptoms, have a pivotal role in the clinical presentation of combat-related PTSD among veterans. Depressive and posttraumatic symptoms constitute two separate diagnostic entities, but with meaningful between-disorder connections, suggesting two mutually-influential systems.

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Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges

Molecules ◽

10.3390/molecules24173099 ◽

2019 ◽

Vol 24 (17) ◽

pp. 3099 ◽

Cited By ~ 3

Author(s):

Xuan ◽

Li ◽

Zhang ◽

Song

Keyword(s):

State Of The Art ◽

Dimensional Space ◽

Nonnegative Matrix ◽

Superior Performance ◽

Pancreatic Cancers ◽

Node Attribute ◽

Disease Associations ◽

Node Attributes ◽

Novel Method ◽

Low Dimensional

Identifying disease-associated microRNAs (disease miRNAs) contributes to the understanding of disease pathogenesis. Most previous computational biology studies focused on multiple kinds of connecting edges of miRNAs and diseases, including miRNA–miRNA similarities, disease–disease similarities, and miRNA–disease associations. Few methods exploited the node attribute information related to miRNA family and cluster. The previous methods do not completely consider the sparsity of node attributes. Additionally, it is challenging to deeply integrate the node attributes of miRNAs and the similarities and associations related to miRNAs and diseases. In the present study, we propose a novel method, known as MDAPred, based on nonnegative matrix factorization to predict candidate disease miRNAs. MDAPred integrates the node attributes of miRNAs and the related similarities and associations of miRNAs and diseases. Since a miRNA is typically subordinate to a family or a cluster, the node attributes of miRNAs are sparse. Similarly, the data for miRNA and disease similarities are sparse. Projecting the miRNA and disease similarities and miRNA node attributes into a common low-dimensional space contributes to estimating miRNA-disease associations. Simultaneously, the possibility that a miRNA is associated with a disease depends on the miRNA’s neighbour information. Therefore, MDAPred deeply integrates projections of multiple kinds of connecting edges, projections of miRNAs node attributes, and neighbour information of miRNAs. The cross-validation results showed that MDAPred achieved superior performance compared to other state-of-the-art methods for predicting disease-miRNA associations. MDAPred can also retrieve more actual miRNA-disease associations at the top of prediction results, which is very important for biologists. Additionally, case studies of breast, lung, and pancreatic cancers further confirmed the ability of MDAPred to discover potential miRNA–disease associations.

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MultiSourcDSim: an integrated approach for exploring disease similarity

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0968-8 ◽

2019 ◽

Vol 19 (S6) ◽

Author(s):

Lei Deng ◽

Danyi Ye ◽

Junmin Zhao ◽

Jingpu Zhang

Keyword(s):

Integrated Approach ◽

Data Sources ◽

Related Data ◽

The Past ◽

Multiple Data ◽

Disease Similarity ◽

Disease Associations ◽

Novel Method ◽

Similarity Networks ◽

Few Data

Abstract Background A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This might further imply treatment that can be appropriated from one disease to another. During the past decades, a number of approaches for calculating disease similarity have been developed. However, most of them are designed to take advantage of single or few data sources, which results in their low accuracy. Methods In this paper, we propose a novel method, called MultiSourcDSim, to calculate disease similarity by integrating multiple data sources, namely, gene-disease associations, GO biological process-disease associations and symptom-disease associations. Firstly, we establish three disease similarity networks according to the three disease-related data sources respectively. Secondly, the representation of each node is obtained by integrating the three small disease similarity networks. In the end, the learned representations are applied to calculate the similarity between diseases. Results Our approach shows the best performance compared to the other three popular methods. Besides, the similarity network built by MultiSourcDSim suggests that our method can also uncover the latent relationships between diseases. Conclusions MultiSourcDSim is an efficient approach to predict similarity between diseases.

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A constrained probabilistic matrix decomposition method for predicting miRNA-disease associations

Current Bioinformatics ◽

10.2174/1574893615999200801014239 ◽

2020 ◽

Vol 15 ◽

Author(s):

Xinguo Lu ◽

Yan Gao ◽

Zhenghao Zhu ◽

Li Ding ◽

Xinyu Wang ◽

...

Keyword(s):

Matrix Decomposition ◽

Disease Diagnosis ◽

Similarity Network ◽

Diagnosis And Prognosis ◽

Non Coding Rna ◽

Disease Similarity ◽

Disease Associations ◽

Novel Method ◽

New Perspective ◽

All Diseases

: MicroRNA is a type of non-coding RNA molecule whose length is about 22 nucleotides. The growing evidence shows that microRNA makes critical regulations in the development of complex diseases, such as cancers, cardiovascular diseases. Predicting potential microRNA-disease associations can provide a new perspective to achieve a better scheme of disease diagnosis and prognosis. However, there is a challenge to predict some potential essential microRNAs only with few known associations. To tackle this, we propose a novel method, named as constrained strategy for predicting microRNA-disease associations called CPMDA, in heterogeneous omics data. Here, we firstly construct disease similarity network and microRNA similarity network to preprocess the microRNAs with none available associations. Then, we apply probabilistic factorization to obtain two feature matrices of microRNA and disease. Meanwhile, we formulate a similarity feature matrix as constraints in the factorization process. Finally, we utilize obtained feature matrixes to identify potential associations for all diseases. The results indicate that CPMDA is superior over other methods in predicting potential microRNA-disease associations. Moreover, the evaluation show that CPMDA has a strong effect on microRNAs with few known associations. In case studies, CPMDA also demonstrated the effectiveness to infer unknown microRNAdisease associations for those novel diseases and microRNAs.

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A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks

Methods ◽

10.1016/j.ymeth.2017.05.024 ◽

2017 ◽

Vol 124 ◽

pp. 69-77 ◽

Cited By ~ 13

Author(s):

Wei Peng ◽

Wei Lan ◽

Jiancheng Zhong ◽

Jianxin Wang ◽

Yi Pan

Keyword(s):

Disease Gene ◽

Disease Associations ◽

Novel Method ◽

Environment Factor

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Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction

Frontiers in Genetics ◽

10.3389/fgene.2021.720327 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jia Qu ◽

Chun-Chun Wang ◽

Shu-Bin Cai ◽

Wen-Di Zhao ◽

Xiao-Long Cheng ◽

...

Keyword(s):

Random Walk ◽

Heterogeneous Networks ◽

Disease Association ◽

Random Walk With Restart ◽

Candidate Mirnas ◽

Biased Random Walk ◽

Disease Associations ◽

And Performance ◽

Function Similarity ◽

Leave One Out

Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.

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Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations

Frontiers in Genetics ◽

10.3389/fgene.2021.754425 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaoyu Yang ◽

Linai Kuang ◽

Zhiping Chen ◽

Lei Wang

Keyword(s):

Matrix Factorization ◽

Cross Validation ◽

Cosine Similarity ◽

Disease Associations ◽

Novel Method ◽

Inflammatory Bowel ◽

Leave One Out ◽

Association Matrix ◽

Similarity Matrices

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.

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SimExact – An Efficient Method to Compute Function Similarity Between Proteins Using Gene Ontology

Current Bioinformatics ◽

10.2174/1574893614666191017092842 ◽

2020 ◽

Vol 15 (4) ◽

pp. 318-327

Author(s):

Najmul Ikram ◽

Muhammad Abdul Qadir ◽

Muhammad Tanvir Afzal

Keyword(s):

Gene Ontology ◽

High Speed ◽

Sequence Similarity ◽

Query Protein ◽

Online Tool ◽

Compute Function ◽

Novel Method ◽

Function Similarity ◽

Functional Prototype ◽

Ranked List

Background: The rapidly growing protein and annotation databases necessitate the development of efficient tools to process this valuable information. Biologists frequently need to find proteins similar to a given protein, for which BLAST tools are commonly used. With the development of biomedical ontologies, e.g. Gene Ontology, methods were designed to measure function (semantic) similarity between two proteins. These methods work well on protein pairs, but are not suitable for protein query processing. Objective: Our aim is to facilitate searching of similar proteins in an acceptable time. Methods: A novel method SimExact for high speed searching of functionally similar proteins has been proposed. Results: The experiments of this study show that SimExact gives correct results required for protein searching. A fully functional prototype of an online tool (www.datafurnish.com/protsem.php) has been provided that generates a ranked list of the proteins similar to a query protein, with a response time of less than 20 seconds in our setup. SimExact was used to search for protein pairs having high disparity between function similarity and sequence similarity. Conclusion: SimExact makes such searches practical, which would not be possible in a reasonable time otherwise.

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