How Similar is Similar

Basuki, Thomas Anung; Antaputra, Benediktus Giovanito

doi:10.1145/3396730.3396736

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Within this investigation, we assess the influence on the features and performance of CA-CMA by comparing various classifier models, aiming to determine the most suitable classification model. Specifically, we made no alterations to the feature extraction methods while substituting the DNN and CNN fusion model with five distinct classifiers, including KNN (K-nearest neighbor) [ 35–37 ], LR (logistic regression) [ 38 , 39 ], RF (rotation forest) [ 40–42 ], SVM (support vector machine) [ 39 , 43 ], AdaBoost algorithm [ 44 ] and GBDT (gradient boosting decision tree) [ 45–47 ] for research. Table 5 showcases the average outcomes obtained from the 5-fold CV experiments conducted by the aforementioned models on the identical dataset, and this information is also depicted in Figure 7 .…”

Section: Resultsmentioning

confidence: 99%

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA–miRNA associations

Guo,

Wang,

You

et al. 2024

Briefings in Bioinformatics

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Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA–MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA–miRNA interactions. Subsequently, it utilized all circRNA–miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA–miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

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