IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion.
Math Biosci Eng
; 20(6): 10659-10674, 2023 Apr 13.
Article
in English
| MEDLINE | ID: covidwho-2324457
ABSTRACT
To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases colon cancer, kidney cancer, and lung cancer.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Colonic Neoplasms
/
MicroRNAs
Type of study:
Etiology study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Math Biosci Eng
Year:
2023
Document Type:
Article
Affiliation country:
Mbe.2023471
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