ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction
نویسندگان
چکیده
منابع مشابه
WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.
Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various ...
متن کاملMCMDA: Matrix completion for MiRNA-disease association prediction
Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective met...
متن کاملGRMDA: Graph Regression for MiRNA-Disease Association Prediction
Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studi...
متن کاملHGIMDA: Heterogeneous graph inference for miRNA-disease association prediction
Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an importa...
متن کاملLRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction
Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs' potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively i...
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ژورنال
عنوان ژورنال: RNA Biology
سال: 2018
ISSN: 1547-6286,1555-8584
DOI: 10.1080/15476286.2018.1460016