A marriage of formal methods and LLMs seeks to harness the strengths of both.

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

The framework predicts how proteins will function with several interacting mutations and finds combinations that work well ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications after stem cell and bone marrow transplants, according to new research ...

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20100 ...

This study is a valuable contribution that comprehensively identifies and characterizes LC3B-binding peptides through a bacterial cell-surface display screen covering approximately 500,000 human ...

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...

Systematic analyses show that normative model performance strongly depends on sample size and covariate distributions, larger samples yield more stable fits, while misaligned covariates introduce ...

Can you design a mechanism that will trace out the shape of a heart? How about the shape of a moon, or a star? Mechanism ...