Protein engineering through machine-learning-guided directed evolution enables the
optimization of protein functions. Machine-learning approaches predict how sequence maps …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

Rational protein engineering requires a holistic understanding of protein function. Here, we
apply deep learning to unlabeled amino-acid sequences to distill the fundamental features …

Predicting the function of a protein from its amino acid sequence is a long-standing
challenge in bioinformatics. Traditional approaches use sequence alignment to compare a …

Understanding the relationship between amino acid sequence and protein function is a long-
standing challenge with far-reaching scientific and translational implications. State-of-the-art …

The field of protein data mining has been growing rapidly in the last years. To characterize
proteins and determine their function from their amino acid sequences are challenging and …

With the massive influx of multimodal data in the last decade, the role of data analytics in
health informatics has grown rapidly. Deep learning (DL) is defined as a technology based …

Directed evolution of proteins has been the most effective method for protein engineering.
However, a new paradigm is emerging, fusing the library generation and screening …

The advancements in next-generation sequencing technologies have given rise to large-
scale, open-source protein databases consisting of hundreds of millions of sequences …

Recently, Deep Learning has been applied to many medical domains, such as medical
image analysis, bioinformatic, biochemistry, drug design, and so forth, to improve the …