AI Model Combines Transformers and Language Embeddings to Predict miRNA–Disease Links
A new transformer-based framework called THIMDA fuses multi-modal microRNA and disease representations to predict disease associations with high accuracy.
A new transformer-based framework called THIMDA fuses multi-modal microRNA and disease representations to predict disease associations with high accuracy.
A lung cancer study on buparlisib and eukaryotic elongation factor-2 has been retracted after tumors in the animal experiments exceeded ...
Researchers engineered a thiourea-bridged cytisine derivative, YU-C-ThioU-9, that overwhelms the antioxidant defenses of lung cancer cells by disrupting Nrf2-Keap1 and ...
Researchers built and validated a nomogram that predicts survival and identifies which elderly female lung cancer patients with bone metastases ...
Researchers engineered an attenuated Pseudomonas aeruginosa strain to inject functional Cre recombinase into mouse lung cell nuclei, triggering gene recombination ...
Researchers in India have developed a hybrid CNN-Transformer deep learning model that classifies lung cancer and its major subtypes from ...
A prospective study of 327 lung cancer patients found that higher daily step counts measured by smartphone during radiotherapy were ...
The Phase III LONESTAR trial found that adding radiation or surgery after nivolumab plus ipilimumab did not improve survival in ...
A retrospective cohort study of 1,674 Chinese lung cancer patients identifies TP53 and TP53-MET co-mutations as independent predictors of poorer ...
A new review maps how the CD73-adenosine axis drives immune suppression in non-small cell lung cancer and how blocking it ...
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine