The development of powerful natural language models has improved the ability to learn
meaningful representations of protein sequences. In addition, advances in high-throughput …

Protein language models (PLMs) convert amino acid sequences into the numerical
representations required to train machine learning models. Many PLMs are large (> 600 …

The discovery of non-coding RNAs (ncRNAs) has expanded our comprehension of RNAs'
inherent nature and capabilities. The intricate three-dimensional structures assumed by …

The scattering transform is a multilayered wavelet-based architecture that acts as a model of
convolutional neural networks. Recently, several works have generalized the scattering …

Geometric deep learning has made great strides towards generalizing the design of
structure-aware neural networks from traditional domains to non-Euclidean ones, giving rise …

Although existing deep learning models perform remarkably well at predicting
physicochemical properties and binding affinities, the generation of new molecules with …

The development of powerful natural language models have increased the ability to learn
meaningful representations of protein sequences. In addition, advances in high-throughput …

Synthetic biologists and molecular programmers design novel nucleic acid reactions, with
many potential applications. Good visualization tools are needed to help domain experts …

Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node
classification and graph classification. However, much less work has been done on signal …

The development of powerful natural language models have increased the ability to learn
meaningful representations of protein sequences. In addition, advances in high-throughput …