Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

Background And Objectives: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts.

Redox status and metabolomic profiling of thioredoxin reductase inhibitors and 4 kGy ionizing radiation-exposed Deinococcus radiodurans.

The gram-positive bacterium Deinococcus radiodurans can survive under extreme ionizing radiation environment. This study aims to rationalize the role of redox balance, antioxidant status, and metabolite content on the radiation survival of D. radiodurans.