Language models have enabled a new era of biological sequence modeling. However,
extracting meaningful sequence-level embeddings from these models remains challenging …

The generation of high-performance enzyme variants with desired physicochemical and
functional properties presents a formidable challenge in the field of protein engineering …

The propensity of protein sites to be occupied by any of the 20 amino acids is known as site-
specific amino acid preferences (SSAP). Under the assumption that SSAP are conserved …

Coastal reclamation engineering structures are exerting significant isolation effects on
dynamics of local species. In this research, a population dynamics model was developed for …

Deep generative models have revolutionized the field of artificial intelligence, fundamentally
changing how we generate novel objects that imitate or extrapolate from training data, and …

The quest to create customized protein sequences with specific functions holds great
promise across diverse fields, from healthcare to sustainable energy. While Next Generation …

Thanks to the explosion of available protein sequence data, driven by next-generation
sequencing, unsupervised machine learning models can be harnessed to learn protein …

In considering evolution of transcribed regions, regulatory sequences, and other genomic
loci, we are often faced with a situation in which the number of allelic states greatly exceeds …

The space of possible proteins is vast. For all but the smallest proteins, the number of
sequences exceeds the number of atoms in the observable universe. Evolution—through …

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities
that can transform the 9 individual and combined effects of mutations. For example …