Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

It is currently challenging to analyze single-cell data consisting of many cells and samples,
and to address variations arising from batch effects and different sample preparations. For …

Infectious diseases, including vector-borne diseases transmitted by arthropods, are a
leading cause of morbidity and mortality worldwide. In the era of big data, addressing broad …

Abnormality detection is a challenging task due to the dependence on a specific context and
the unconstrained variability of practical scenarios. In recent years, it has benefited from the …

In this paper, we present a Heterogeneous Network with Multi-United-Memory (HN-MUM)
module, which integrates motion and appearance to solve the Video Anomaly Detection …

The detection of brain disease is an essential issue in medical and research areas. Deep
learning techniques have shown promising results in detecting and diagnosing brain …

Video anomaly detection is a recent focus of computer vision research thanks to the rarity
and uncertainty of anomalous events. However, most existing research works are limited to …

Computational-intelligence methods in bioinformatics and systems biology show promising
potential for leveraging abundant, large-scale molecular data. These methods can facilitate …

With the rapid development of high-throughput technologies, systems biology is now
embracing a great opportunity made possible by the increased accumulation of data …

The prediction of protein structure has a major role in drugs design and network
pharmacology. However, complexity of the protein structure, time consumption and …