Tag: Machine learning

Volume 43 of SLAS Discovery presents AI-guided screening, automated platforms and new cell-based assays that accelerate drug discovery across infectious ...

EIHR-PROT combines protein language model embeddings with homology search priors through a learned gate that adjusts fusion weights based on ...

A new framework called PoAC allows users to define specific clustering goals, outperforming traditional AutoML methods in visualization and anomaly ...

A study evaluating South American precipitation records from 1982 to 2020 finds that while combined climate modes yield substantial regional ...

A retrospective study evaluating a gradient boosting model incorporating heart function and inflammatory markers found moderate discrimination but no clear ...

A new framework called AMG improves unsupervised anomaly detection in complex attributed multiplex heterogeneous networks by integrating multi-channel graph convolutional ...

A new machine learning model developed by researchers predicts the risk of recurrence in patients with borderline ovarian tumors undergoing ...

A new self-supervised framework called Subtype-DIVIDE uses multi-omic data and random-walk-refined contrastive learning to identify robust cancer subtypes with significant ...

A prospective diagnostic cohort study validates an AI-ECG algorithm as an adjunct to troponin tests for early heart attack diagnosis ...

Researchers combined hyperspectral imaging with explainable machine learning to accurately identify authentic Rubia cordifolia medicinal roots and trace their geographical ...