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Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations

Published in Frontiers in Genetics

More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease as...

Extending the Capabilities of Data-Driven Reduced-Order Models to Make Predictions for Unseen Scenarios: Applied to Flow...

Published in Frontiers in Physics

We present a data-driven or non-intrusive reduced-order model (NIROM) which is capable of making predictions for a significantly larger domain than the one used to generate the snapshots or training data. This development relies on the combination of a novel way of sampling the training data (which frees the NIROM from its dependency on the origina...

Cardiomyocyte Cell-Cycle Regulation in Neonatal Large Mammals: Single Nucleus RNA-Sequencing Data Analysis via an Artifi...

Published in Frontiers in Bioengineering and Biotechnology

Adult mammalian cardiomyocytes have very limited capacity to proliferate and repair the myocardial infarction. However, when apical resection (AR) was performed in pig hearts on postnatal day (P) 1 (ARP1) and acute myocardial infarction (MI) was induced on P28 (MIP28), the animals recovered with no evidence of myocardial scarring or decline in cont...

Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer

Published in Frontiers in Oncology

Objective Post-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data ...

Trainable Communication Systems Based on the Binary Neural Network

Published in Frontiers in Communications and Networks

End-to-end learning of the communication system regards the transmitter, channel, and receiver as a neural network-based autoencoder. This approach enables joint optimization of both the transmitter and receiver and can learn to communicate more efficiently than model-based ones. Despite the achieved success, high complexity is the major disadvanta...

Long Short-Term Memory-Based Music Analysis System for Music Therapy

Published in Frontiers in Psychology

Music can express people’s thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatm...

A Data-Driven Semi-Supervised Soft-Sensor Method: Application on an Industrial Cracking Furnace

Published in Frontiers in Chemical Engineering

The cracking furnace is the key equipment of the ethylene unit. Coking in furnace tubes results from the generation of coke during cracking, which will compromise the heat transfer efficiency and lead to shape change of tubes. In order to keep the cracking furnace operating economically and safely, the engineers need to decoke according to the surf...

DOC-IDS: A Deep Learning-Based Method for Feature Extraction and Anomaly Detection in Network Traffic

With the growing diversity of cyberattacks in recent years, anomaly-based intrusion detection systems that can detect unknown attacks have attracted significant attention. Furthermore, a wide range of studies on anomaly detection using machine learning and deep learning methods have been conducted. However, many machine learning and deep learning-b...