MicroRNA-Disease Association Prediction Using DeepWalk Network Topological Similarity
Explore how network topological similarity based on DeepWalk enhances miRNA-disease association prediction. The method shows promising results in predicting complex diseases like breast cancer, lung cancer, and prostatic cancer. Utilizing deep learning, the approach demonstrates superior performance through similarity measures within the miRNA-disease network.
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Presentation Transcript
Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk Guanghui Li, Jiawei Luo, Qiu Xiao, Cheng Liang, Pingjian Ding, Buwen Cao IEEE Access, Volume 5, 2017 Speaker: Jheng-Yan Lyu Date: Apr. 7, 2020
Abstract (1/2) Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association predictionbasedontheintegrationofadditionalbiologicalinformationofdiseasesandmiRNAs,such asdiseasesemanticsimilarityandmiRNAfunctionalsimilarity.However,thesemethodsdonotwork wellwhenthisinformationisunavailable.Inthispaper,wepresentasimilarity-basedmiRNA-disease predictionmethodthatenhancestheexistingassociationdiscoverymethodsthroughatopology-based similaritymeasure.DeepWalk,adeeplearningmethod,isutilizedinthispapertocalculatesimilarities withinamiRNA-diseaseassociationnetwork. 2
Abstract (2/2) Itshowssuperiorpredictiveperformancefor22complexdiseases,withareaundertheROCcurve scoresrangingfrom0.805to0.937byusingfive-foldcross-validation.Inaddition,casestudieson breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latentmiRNA-diseasepairs. 3
Workflow 4
Similarity Learning 2123 6
Similarity Learning Disease-Based Similarity Inference 8
Results 9
Results 10
