M3SpaDE (Multi-Modal Model for predicting Spatial Drug Efficacy) is a versatile computational framework designed for predicting drug sensitivity in spatial transcriptomics data. It is resolution-agnostic, capable of processing data ranging from single-cell to spot-level resolutions, and supports generalizable prediction of responses to previously unseen drugs based on their chemical structures. M3SpaDE enables the following tasks: Binarized Sensitivity PredictionPerforms binary classification of drug sensitivity at the single-cell or spot level (Sensitive vs. Resistant). Spatial Autocorrelation AnalysisQuantifies global spatial dependency and clustering patterns using Join Count statistics. Combinatorial Therapy AssessmentPredicts and evaluates drug sensitivity outcomes for drug combinations.
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".