A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)

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dc.contributor.authorToprak, Ahmet
dc.date.accessioned2025-01-21T14:20:38Z
dc.date.available2025-01-21T14:20:38Z
dc.date.issued2022
dc.description.abstractmiRNAs, a subclass of non-coding small RNAs, are about 18-22 nucleotides long. It has been revealed that miRNAs are responsible many diseases such as cancer. Therefore, great efforts have been made recently by researchers to explore possible relationships between miRNAs and diseases. Experimental studies to identify new disease-associated miRNAs are very expensive and at the same time a long process. Therefore, to determine the relationships between miRNA and disease many computational methods have been developed. In this paper, a new method for the identification of miRNA-disease associations based on space projection and label propagation (SPLPMDA) is proposed. The forecast the precision of SPLPMDA was demonstrated using 5-fold cross-validation and LOOCV techniques. Values of 0.9333 in 5-fold cross validation and 0.9441 in LOOCV were obtained. Moreover, case studies on breast neoplasms and lymphoma were performed to further confirm the predictive reliability of SPLPMDA.
dc.description.abstractmiRNAs, a subclass of non-coding small RNAs, are about 18-22 nucleotides long. It has been revealed that miRNAs are responsible many diseases such as cancer. Therefore, great efforts have been made recently by researchers to explore possible relationships between miRNAs and diseases. Experimental studies to identify new disease-associated miRNAs are very expensive and at the same time a long process. Therefore, to determine the relationships between miRNA and disease many computational methods have been developed. In this paper, a new method for the identification of miRNA-disease associations based on space projection and label propagation (SPLPMDA) is proposed. The forecast the precision of SPLPMDA was demonstrated using 5-fold cross-validation and LOOCV techniques. Values of 0.9333 in 5-fold cross validation and 0.9441 in LOOCV were obtained. Moreover, case studies on breast neoplasms and lymphoma were performed to further confirm the predictive reliability of SPLPMDA.
dc.identifier.dergipark1217754
dc.identifier.doi10.29137/umagd.1217754
dc.identifier.issn1308-5514
dc.identifier.issue3-234
dc.identifier.startpage243
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/2826219
dc.identifier.urihttps://dergipark.org.tr/tr/pub/umagd/issue/74185/1217754
dc.identifier.urihttps://doi.org/10.29137/umagd.1217754
dc.identifier.urihttps://hdl.handle.net/20.500.12587/19245
dc.identifier.volume1
dc.language.isoen
dc.publisherKırıkkale Üniversitesi
dc.relation.ispartofUluslararası Mühendislik Araştırma ve Geliştirme Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectmiRNA
dc.subjectdisease
dc.subjectmiRNA-disease association
dc.subjectspace projection
dc.subjectlabel propagation
dc.subjectmiRNA
dc.subjectdisease
dc.subjectmiRNA-disease association
dc.subjectspace projection
dc.subjectlabel propagation
dc.subjectEngineering
dc.titleA Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)
dc.title.alternativeA Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)
dc.typeArticle

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