Sparse numerical datasets are dominant in fields such as applied mathematics, astronomy, finance, and healthcare, presenting challenges due to their high dimensionality and sparse distribution. The predominance of zero values complicates optimal feat...
The rapid growth of multiomics data has revolutionized our ability to investigate disease mechanisms, yet significant challenges persist in achieving meaningful integration due to inherent data heterogeneity, characteristic sparsity patterns, and the...
The burgeoning necessity for copious and diverse electrocardiogram (ECG) datasets for deep learning applications in clinical diagnostics has been impeded by the confidential nature of patient data. Related works have shown the effectiveness of additi...
Proceedings of the National Academy of Sciences of the United States of America
Oct 6, 2025
Deep generative models have demonstrated success in learning the protein sequence to function relationship and designing synthetic sequences with engineered functionality. We introduce the Protein Transformer Variational AutoEncoder (ProT-VAE) as an ...
To solve the problems of existing encrypted traffic classification methods, such as the need for large-scale training data, high computational costs, and poor generalization ability, an encrypted traffic classification method based on autoencoders an...
Harmonizing multisite functional magnetic resonance imaging (fMRI) data is crucial for eliminating site-specific variability that hinders the generalizability of machine learning models. Traditional harmonization techniques, such as ComBat, depend on...
Self-supervised learning (SSL) has gained significant attention in medical imaging for its ability to leverage large amounts of unlabeled data for effective model pretraining. Among SSL methods, the masked autoencoder (MAE) has proven robust in learn...
Journal of chemical information and modeling
Aug 21, 2025
Predicting compound-protein interaction (CPI) plays a critical role in drug discovery and development, but traditional screening experiments consume much time and resources. Therefore, deep learning methods for CPI prediction are popular now. However...
Proceedings of the National Academy of Sciences of the United States of America
Aug 19, 2025
Foundation models in biology-particularly protein language models (PLMs)-have enabled ground-breaking predictions in protein structure, function, and beyond. However, the "black-box" nature of these representations limits transparency and explainabil...
As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-mod...
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