M Cabrera, D Ryan, M Erlewyn-Lajeunesse, M Levin, A Martínez-Cañavate, C Villaizán Pérez, E Angier, J Trujillo, J M Cárdenas-Rebollo, L Reali
Global assessment of the knowledge and confidence in managing allergic disorders among primary care pediatricians across Europe: An EAACI task force report
Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology
despite significant advances tumor biology clinical therapeutics metastasis remains primary cause cancerrelated deaths rnaseq technology used extensively study metastatic cancer characteristics challenges persist acquiring adequate transcriptomic data overcome challenge propose metgen generative contrastive learning tool based deep learning model metgen generates synthetic metastatic cancer expression profiles using primary cancer normal tissue expression data results demonstrate metgen generates comparable samples actual metastatic cancer samples cancer tissue classification yields performance rates 998 02 950 23 respectively benchmark analysis suggests proposed model outperforms traditional generative models variational autoencoder metastatic subtype classification generated samples show 976 predicting power compared true metastatic samples additionally demonstrate metgens interpretability using metastatic prostate cancer metastatic breast cancer metgen learned highly relevant signatures cancer tissue tumor microenvironments immune responses metastasis process potentially foster comprehensive understanding metastatic cancer biology development metgen represents significant step toward study metastatic cancer biology providing generative model identifies candidate therapeutic targets treatment metastatic cancer