Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...

Most data teams discover quality problems the same way: a dashboard looks wrong, a stakeholder files a ticket, and an engineer traces the damage backward through the pipeline. By then, the bad data ...

Abstract: This study explores the implementation of advanced machine learning techniques to enhance the integration of renewable energy into smart grids, focusing specifically on predicting solar ...

Abhinav Piratla, an AI security architect, is closing the critical gap in medical device protection. Discover how his pioneering work, including AP-GUARD, secures life-critical AI systems against real ...

Abstract: Vibration signals are generally utilized for machinery fault diagnosis to perform timely maintenance and then reduce losses. Thus, the feature extraction on one-dimensional vibration signals ...

Researchers have developed an artificial intelligence model that predicts crime more accurately than several existing approaches by combining information about where crimes occur, when they happen and ...

MST-VAE is an unsupervised learning approach for anomaly detection in multivariate time series. Inspired by InterFusion paper, we propose a simple yet effective multi-scale convolution kernels applied ...

Raindrops form inside clouds when tiny particles of water collide and stick together, forming larger droplets that eventually ...

The Martin-Hopkins equation to assess low-density lipoprotein (LDL) cholesterol levels in blood samples has been used by ...

RNA has emerged as one of the most promising molecules in modern medicine, enabling advances from mRNA vaccines and gene ...