Deep learning methods are a class of machine learning techniques capable of identifying
highly complex patterns in large datasets. Here, we provide a perspective and primer on …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

This Perspective provides examples of current and future applications of deep learning in
pharmacogenomics, including: identification of novel regulatory variants located in …

The rise of omics techniques has resulted in an explosion of molecular data in modern
biomedical research. Together with information from medical images and clinical data, the …

Background In the human genome, 98% of DNA sequences are non-protein-coding regions
that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of …

Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class
of methods, based on artificial neural networks, quickly became popular due to its …

Convolutionary neural network (CNN) is a popular choice for supervised DNA motif
prediction due to its excellent performances. To employ CNN, the input DNA sequences are …

Classification of breast cancer subtypes using multi-omics profiles is a difficult problem since
the data sets are high-dimensional and highly correlated. Deep neural network (DNN) …

Eukaryotic promoter regions are frequently divergently transcribed in vivo, but it is unknown
whether the resultant antisense RNAs are a mechanistic by-product of RNA polymerase II …

In recent years, deep learning has been widely used in diverse fields of research, such as
speech recognition, image classification, autonomous driving and natural language …