As a data-driven science, genomics largely utilizes machine learning to capture
dependencies in data and derive novel biological hypotheses. However, the ability to extract …

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

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 …

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

Abstract The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN. itps. ncku. edu.
tw/) is an effective resource for predicting regulatory elements and reconstructing …

Deep learning is a new area of machine learning research. Deep learning technology
applies the nonlinear and advanced transformation of model abstraction into a large …

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a central method in
epigenomic research. Genome-wide analysis of histone modifications, such as enhancer …

Technological advances in genomics and imaging have led to an explosion of molecular
and cellular profiling data from large numbers of samples. This rapid increase in biological …

Although Hi-C technology is one of the most popular tools for studying 3D genome
organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and …

Innovations in data science and artificial intelligence/machine learning (AI/ML) have a
central role to play in supporting global efforts to combat COVID-19. The versatility of AI/ML …