Special Issue Information
Biosciences are becoming increasingly data-centric and data intensive. Diagnostics and related methodologies that once exclusively relied on experts to characterize cells, tissues, and medical information are now using big data computational techniques for decision making. Deep learning encompasses machine learning algorithms that combine a network of successive processing layers of data representation. Modern deep learning can expand to tens or hundreds of layers depending on the complexity of the raw data and the learning success of the layered representations. The whole process is achieved via models that are called neural networks, inspired by the processing of information in the brain.
Deep learning has shown remarkable success in numerous life sciences disciplines, but amid concerns for lack of biological context. Nevertheless, as the field of biosciences rapidly evolves, so do the data and the computational resources available to researchers. Thus, the emerging combination of deep learning with biosciences, although challenging, can lead to high-impact goals in healthcare analytics, medical diagnosis, research in biology (including biophysics and biochemistry), personalized medicine, and pharmaceutical development.
This Special Issue is open for innovative contributions related to the above-mentioned topics. Manuscripts discussing the ethical considerations of deep learning in healthcare are also welcome.