Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms.

ABSTRACT: Increasing evidence indicates that miRNAs play a vital role in biological processes and are closely related to various human diseases. Research on miRNA-disease associations is helpful not only for disease prevention, diagnosis and treatment, but also for new drug identification and lead compound discovery. A novel sequence- and symptom-based random forest algorithm model (Seq-SymRF) was developed to identify potential associations between miRNA and disease. Features derived from sequence information and clinical symptoms were utilized to characterize miRNA and disease, respectively. Moreover, the clustering method by calculating the Euclidean distance was adopted to construct reliable negative samples. Based on the fivefold cross-validation, Seq-SymRF achieved the accuracy of 98.00%, spec

SUBMITTER: Li J 

PROVIDER: S-EPMC7578641 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature