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 …

ML has been the main contributor to the recent resurgence of artificial intelligence. The most
essential piece in modern ML technology is DL. DL is founded on artificial neural networks …

To improve the issue of low-frequency and high-frequency components from feature maps
being treated equally in existing image super-resolution reconstruction methods, the paper …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

In offline model-based optimization, we strive to maximize a black-box objective function by
only leveraging a static dataset of designs and their scores. This problem setting arises in …

The tremendous amount of biological sequence data available, combined with the recent
methodological breakthrough in deep learning in domains such as computer vision or …

Abstract Knowledge of the specificity of DNA-protein binding is crucial for understanding the
mechanisms of gene expression, regulation and gene therapy. In recent years, deep …

Proteins are some of the most fascinating and challenging molecules in the universe, and
they pose a big challenge for artificial intelligence. The implementation of machine …

Identifying genome-wide binding events between circular RNAs (circRNAs) and RNA-
binding proteins (RBPs) can greatly facilitate our understanding of functional mechanisms …

Zinc-finger protein (ZFP) transcription factors are among the largest families of transcription
factors in plants. They participate in various biological processes such as apoptosis …