To create their contactless approach, the researchers created a machine learning algorithm capable of analyzing aspects of ...

Machine learning models reveal that histone marks are predictive of gene expression across human cell types and highlight important nuances between natural control and the effects of CRISPR-Cas9-based ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

A new artificial intelligence model in the US, SleepFM, has found that patterns in human sleep can be used to predict a ...

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...

In the past decade, cloud-scale analytics tools have transformed the digital fight against deforestation. Instead of manual ...

The SleepFM model reveals how sleep analysis can predict disease risk, offering insights into sleep's role as a vital health ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Buildings produce a large share of New York's greenhouse gas emissions, but predicting future energy demand—essential for ...

The XGBoost model predicts hyperglycemia risk in psoriasis patients with high accuracy, achieving an AUC of 0.821 in the training set. A web-based calculator was developed to facilitate personalized ...

The GC–MS dataset was integrated with the sensory data using a series of exploratory and predictive multivariate statistical ...

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...