Journal article

TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction

Korawich Uthayopas, Alex GC de Sa, Azadeh Alavi, Douglas E Pires, David B Ascher



The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method..

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Awarded by Newton Fund RCUK-CONFAP Grant - Medical Research Council

Awarded by Wellcome Trust

Awarded by Jack Brockhoff Foundation

Awarded by National Health and Medical Research Council (NHMRC) of Australia

Funding Acknowledgements

K.U. was supported by the Melbourne Research Scholarship. A.G.C.d.S. acknowledges the Joe White Bequest Fellowship for its support. D.B.A. and D.E.V.P. were funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MR/M026302/1). D.B.A. was supported by the Wellcome Trust (grant 093167/Z/10/Z), the Jack Brockhoff Foundation (JBF 4186, 2016), and an Investigator Grant from the National Health and Medical Research Council (NHMRC) of Australia (GNT1174405). Supported in part by the Victorian Government's Operational Infrastructure Support Program.