AI-driven framework enables precise prediction of RNA splicing and isoform usage

RNA is the means of translating the genetic code embedded in DNA into proteins, which serve as enzymes, transporters, ...
Tech Xplore

A novel deep learning architecture for multi-source data fusion

Recent years have witnessed the unprecedented development of Industry 4.0 and the Industrial Internet of Things. These two ...
IEEE

Deep Feature Learning for Multi-Omics Clustering Using Supervised Variational Autoencoders with Clinical Information

Abstract: Identifying cancer subtypes through multi-omics clustering holds potential to advance cancer research by uncovering subtype-specific mechanisms. Most existing multi-omics clustering methods ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Scientific Research Publishing

Wen, T. and Zhang, Z. (2018) Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals. IEEE Access, 6, 25399-25410.

ABSTRACT: The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in ...
Frontiers

Recovering manifold representations via unsupervised meta-learning

Manifold representation learning holds great promise for theoretical understanding and characterization of deep neural networks' behaviors through the lens of geometries. However, data scarcity ...
Scientific Research Publishing

Ibebuchi, C.C. and Richman, M.B. (2024) Deep Learning with Autoencoders and LSTM for Enso Forecasting. Cambridge University Press.

ABSTRACT: The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO ...