Graph neural networks for clinical risk prediction based on electronic health records: A survey.

Objective: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks.

Methods: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data.

Standardization of Synthetic Biology Tools and Assembly Methods for and Emerging Yeast Species.

As redesigning organisms using engineering principles is one of the purposes of synthetic biology (SynBio), the standardization of experimental methods and DNA parts is becoming increasingly a necessity. The synthetic biology community focusing on the engineering of has been in the foreground in this area, conceiving several well-characterized SynBio toolkits widely adopted by the community. In this review, the molecular methods and toolkits developed for are discussed in terms of their contributions to the required standardization efforts.