Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

A machine learning model analyzing CpG-based DNA methylation accurately predicted the origin of many different cancer types ...

Machine learning accurately predicts peak and average IOP, aiding glaucoma management by informing treatment decisions. Random forest regression (RFR) outperformed ...

More than a decade ago, researchers launched the BabySeq Project, a pilot program to return newborn genomic sequencing results to parents and measure the effects on newborn care. Today, over 30 ...

This article examines the work of data scientist Sai Prashanth Pathi in AI for credit risk, focusing on explainable machine ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Infinite dilution activity coefficient is a key thermodynamic parameter in solvent design for chemical processes. Although ...