Virtual Cell Challenge: Toward a Turing test for the virtual cell.

Virtual cells are an emerging frontier at the intersection of artificial intelligence and biology. A key goal of these cell state models is predicting cellular responses to perturbations. The Virtual Cell Challenge is being established to catalyze progress toward this goal.

A large-scale benchmark for network inference from single-cell perturbation data.

Mapping biological mechanisms in cellular systems is a fundamental step in early-stage drug discovery that serves to generate hypotheses on what disease-relevant molecular targets may effectively be modulated by pharmacological interventions. With the advent of high-throughput methods for measuring single-cell gene expression under genetic perturbations, we now have effective means for generating evidence for causal gene-gene interactions at scale. However, evaluating the performance of network inference methods in real-world environments is challenging due to the lack of ground-truth knowledge.

Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning.

Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DL-based systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists.