We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy
and residue-residue distance signed error in protein models and uses these predictions to …

The trRosetta structure prediction method employs deep learning to generate predicted
residue‐residue distance and orientation distributions from which 3D models are built. We …

Motivation Learning robust prediction models based on molecular profiles (eg, expression
data) and phenotype data (eg, drug response) is a crucial step toward the development of …

Sexual reproduction roots the eukaryotic tree of life, although its loss occurs across diverse
taxa. Asexual reproduction and clonal lineages persist in these taxa despite theoretical …

Determining the binding locations of regulatory factors, such as transcription factors and histone
modifications, is essential to both basic biology research and many clinical applications. …

ChIP-seq is a technique to determine binding locations of transcription factors, which remains
a central challenge in molecular biology. Current practice is to use a ‘control’ dataset to …

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) is a
widely used method to determine the binding positions of various proteins on the genome in a …

A team of agents that cooperate to solve a problem together can handle many complex
tasks that would not be possible without cooperation. While the benefit is clear, there are still …

Understanding the rules of protein structure folding has always been one of the central goals
in computational biology. Deep learning is gaining popularity in protein machine learning …

Evolutionary Algorithms (EAs) apply the basic forces of biological evolution (differential
fitness, inherited traits with variation, and natural selection) to discover novel solutions to …