A public EMG-IMU dataset produced near-99% benchmark accuracy for attention-enhanced models, while the test design limits what the scores say about unseen users and real-world use.
A machine-learning model identified liver fibrosis more accurately than standard scoring tools in a validation group of people previously infected with Schistosoma japonicum. The result is promising, but the study was cross-sectional and drew on one province in China, so it does not yet show that the model improves care or works elsewhere.
The models tracked measured exon inclusion well in some assays, but interpretable surrogates exposed sequence biases and missed structure-linked changes in selected variants.
A reanalysis of public chickpea RNA-seq datasets found that HDBSCAN produced a more stress-specific drought signal than conventional tests and HN-score meta-analysis, but its performance depended on data richness.
SPALT combines local sensor patterns with a pruned linear model tree, and its overall accuracy advantage came with a smaller model rather than a faster one.
Machine Learning, Volume 114, article number 231 (2025)4 min read