Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

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 …

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal
phenotype and fitness,–. Constructing complete fitness landscapes, in which DNA …

Transcriptional enhancers act as docking stations for combinations of transcription factors
and thereby regulate spatiotemporal activation of their target genes. It has been a long …

Discriminative neural networks offer little or no performance guarantees when deployed on
data not generated by the same process as the training distribution. On such out-of …

Generative adversarial network (GANs) is one of the most important research avenues in the
field of artificial intelligence, and its outstanding data generation capacity has received wide …

Generative models are becoming a tool of choice for exploring the molecular space. These
models learn on a large training dataset and produce novel molecular structures with similar …

Designing biological sequences is an important challenge that requires satisfying complex
constraints and thus is a natural problem to address with deep generative modeling …

Abstract Design of de novo synthetic regulatory DNA is a promising avenue to control gene
expression in biotechnology and medicine. Using mutagenesis typically requires screening …

The basic Generative Adversarial Networks (GAN) model is composed of the input vector,
generator, and discriminator. Among them, the generator and discriminator are implicit …