Federica Eduati

TL;DR: A microfluidic platform that allows testing of multiple drug combinations correctly predicting sensitivity in vivo and it is used on patients biopsies to define effective drugs can pave the way for rapid determination of optimal personalized cancer therapies.

TL;DR: The results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

TL;DR: It is suggested that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes.

TL;DR: Caspo as mentioned in this paper is an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming.

TL;DR: This Mini Review gives an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides, and discusses how this broader vision of the cellular heterogeneity and plasticity of tumors offers the opportunity to rationally design precision immuno-oncology treatments.