Unsupervised learning techniques play a pivotal role in unraveling protein folding landscapes, constructing Markov State Models, expediting replica exchange simulations, and discerning drug binding ...

To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

Autoencoder-driven clustering of intersecting D-brane models via tadpole charge Journal: Journal of High Energy Physics Published: 2024-08-19 DOI: 10.1007/jhep08 (2024)133 Affiliations: 2 Authors: 3 ...

We introduce autoencoder-based clustering algorithms to short text clustering, proposing a novel deep learning-based text clustering framework (TCBPMA). By combining pre-trained models and autoencoder ...

In a previous work, a clustering-based method had been incorporated with the latent feature space of an autoencoder to discover sub-classes of normal data for anomaly detection. However, the work has ...

In particular, gene clustering is used to discover previously unknown mechanisms, contributing to a wide range of applications such as drug discovery, cancer prediction, and diagnosis. Identifying new ...

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Recently, clustering algorithms based on deep AutoEncoder attract lots of attention due to their excellent clustering performance. On the other hand, the success of PCA-Kmeans and spectral clustering ...

Unlike iCluster, autoencoder-based deep learning and PCA were not clustering algorithms, thus the other two were followed by k-means clustering. Taking together, these three methods were able to ...