The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

In the era of big data, transformation of biomedical big data into valuable knowledge has
been one of the most important challenges in bioinformatics. Deep learning has advanced …

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care
problems has received unprecedented attention in the last decade. The technique has …

The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …

Over the past decades, medical image analytics was greatly facilitated by the explosion of
digital imaging techniques, where huge amounts of medical images were produced with …

Background: Interpretation of medical images for the diagnosis and treatment of complex
diseases from high-dimensional and heterogeneous data remains a key challenge in …

Multimodal data-driven approach has emerged as an important driving force for smart
healthcare systems with applications ranging from disease analysis to triage, diagnosis and …

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would
normally require intelligent action by a human. Much of the recent excitement about AI in the …

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators
participating in various biological functions and disease processes. With the widespread …

Machine learning is enabling researchers to analyze and understand increasingly complex
physical and biological phenomena in traditional fields such as biology, medicine, and …