This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...

In this important work, the authors present a new transformer-based neural network designed to isolate and quantify higher-order epistasis in protein sequences. They provide solid evidence that higher ...

Description Snakemake pipeline for automated assembly of third-generation sequencing data, including quality control, de novo assembly, multi-round polishing, and genome optimization. Quality Control ...

Fibonacci sequences are sequences of numbers whose first two elements are 0, 1, and such that, starting from the third number, every element of the sequence is the sum of the previous two. They are of ...

Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today’s models ...

Designing accurate all-atom protein structures is a critical challenge in bioengineering, as it involves generating both 3D structural data and 1D sequence information to define side-chain atom ...

Have you ever found yourself endlessly dragging that little Excel fill handle, trying to populate rows or columns with numbers, dates, or formulas? It’s tedious, time-consuming, and let’s be ...

Over more than three billion years, natural evolution has intricately shaped the proteins we see today. Through countless random mutations and selective pressures, nature has crafted these proteins, ...