iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion
Autor: | Qiu Xiao, Xiwei Tang, Jiancheng Zhong, Jiawei Luo |
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Rok vydání: | 2020 |
Předmět: |
0106 biological sciences
0301 basic medicine medicine.medical_treatment Datasets as Topic Disease Computational biology Biology 01 natural sciences Targeted therapy 03 medical and health sciences Neoplasms Similarity (psychology) Genetics medicine Humans Diagnostic biomarker RNA Neoplasm Molecular Biology Models Statistical Models Genetic Computational Biology RNA Circular General Medicine Special class Human genetics 030104 developmental biology Research Design Algorithms 010606 plant biology & botany |
Zdroj: | Molecular Genetics and Genomics. 296:223-233 |
ISSN: | 1617-4623 1617-4615 |
Popis: | Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately. However, it is tremendously laborious and time-consuming to discover disease-related circRNAs with conventional biological experiments. In this study, we develop an integrative framework, called iCDA-CMG, to predict potential associations between circRNAs and diseases. By incorporating multi-source prior knowledge, including known circRNA-disease associations, disease similarities and circRNA similarities, we adopt a collective matrix completion-based graph learning model to prioritize the most promising disease-related circRNAs for guiding laborious clinical trials. The results show that iCDA-CMG outperforms other state-of-the-art models in terms of cross-validation and independent prediction. Moreover, the case studies for several representative cancers suggest the effectiveness of iCDA-CMG in screening circRNA candidates for human diseases, which will contribute to elucidating the pathogenesis mechanisms and unveiling new opportunities for disease diagnosis and targeted therapy. |
Databáze: | OpenAIRE |
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