Artificial intelligence (AI) models based on deep learning now represent the state of the art
for making functional predictions in genomics research. However, the underlying basis on …

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

Identifying and characterizing mobile genetic elements in sequencing data is essential for
understanding their diversity, ecology, biotechnological applications and impact on public …

Understanding cellular responses to genetic perturbation is central to numerous biomedical
applications, from identifying genetic interactions involved in cancer to developing methods …

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are
recognized by autologous T cells in the host. As neoepitopes are not subject to central …

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly
evolving, making it possible to comprehensively characterize cells and tissues in health and …

How noncoding DNA determines gene expression in different cell types is a major unsolved
problem, and critical downstream applications in human genetics depend on improved …

The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

The reprogramming of somatic cells with defined factors, which converts cells from one
lineage into cells of another, has greatly reshaped our traditional views on cell identity and …