Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

“…In esophageal cancer (EC), the long noncoding RNA ADAMTS9-AS2 effectively suppresses cancer cell proliferation, invasion, and migration processes . Therefore, investigating potential associations between LncRNAs and diseases contributes to the prevention, detection, and treatment of relevant diseases in humans. − While traditional biological experimental methods exhibit high accuracy in discovering potential correlations, the process is intricate and time-consuming. , Hence, capitalizing on the rapid development of computer technology, developing an efficient and convenient computational method for detecting the correlation between LncRNAs and diseases is of significant importance. − Cheng et al presented the first solution for predicting associations in LncRNA-disease relationships. Subsequently, an increasing number of computational prediction models, , including matrix factorization, have been applied to predict LncRNA-disease associations.…”