Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Here we present deep-learning techniques for healthcare, centering our discussion on deep
learning in computer vision, natural language processing, reinforcement learning, and …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

Machine learning, a collection of data-analytical techniques aimed at building predictive
models from multi-dimensional datasets, is becoming integral to modern biological research …

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

Rapid advances in the field of artificial intelligence have profound implications for the
economy as well as society at large. These innovations have the potential to directly …

With a massive influx of multimodality data, the role of data analytics in health informatics
has grown rapidly in the last decade. This has also prompted increasing interests in the …

Deep learning, which is especially formidable in handling big data, has achieved great
success in various fields, including bioinformatics. With the advances of the big data era in …

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 …