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 …

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 …

Predicting the impact of noncoding genetic variation requires interpreting it in the context of
three-dimensional genome architecture. We have developed deepC, a transfer-learning …

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 …

Deciphering the genomic regulatory code of enhancers is a key challenge in biology
because this code underlies cellular identity. A better understanding of how enhancers work …

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

Background In the human genome, distal enhancers are involved in regulating target genes
through proximal promoters by forming enhancer‐promoter interactions. Although recently …

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 …

Crop breeding is one of the main approaches to increase crop yield and improve crop
quality. However, the breeding process faces challenges such as complex data, difficulties …

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare
including research and chemical discoveries. Using large amounts of aggregated data, the …