The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...

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

Advances in mechanistic modeling, machine learning, and biomedical data integration are making it possible to move beyond “one-size-fits-all” evidence and ...

Explore how machine learning in insurance enhances risk assessment, fraud detection, and personalization. ✓ Subscribe for ...

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...

Digital twins revolutionize drug discovery by integrating AI and biological data, enhancing prediction, trial design, and ...

Real-world data (RWD) is transforming clinical research, augmenting existing randomized controlled trial (RCT) data to de-risk studies and improve generalizability. With regulators setting clearer ...

This study addresses endosomal escape in oligonucleotide therapies and how engineered peptides can enhance intracellular drug delivery efficiency.

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

Artificial intelligence (AI) and machine learning (ML) hold significant promise in advancing the field of toxicology by ...

Evolving toxicity assessments for engineered nanoparticles underline the importance of predictive models and life-cycle risk ...