Next clinical data update from the ongoing Phase 1 dose escalation ACESOT-1051 trial anticipated at a medical meeting in Q4 2026, providing a near-term opportunity to demonstrate continued clinical ...

This study evaluates Batch Bayesian Optimization as a robust alternative using Sporosarcina pasteurii to optimize microbial ...

Objectives To identify the latent profiles of health behaviour abilities among patients with haematological malignancies and ...

Abstract: Optimal design is a critical yet challenging task within many applications. This challenge arises from the need for extensive trial and error, often done through simulations or running field ...

Bloomberg is pleased to announce the newest cohort of three early-career researchers who have received the Bloomberg Data Science Ph.D. Fellowship for 2024-2025. Now in its seventh cohort, the ...

Fundamental models are important for design, control, and optimization of chemical systems but often contain many unknown parameters that require estimation from experimental data. Model-based design ...

Optimal Bayesian Experiment Design is for making smart setting choices in measurements. The optbayesexpt python package is for cases with a known parametric model, i.e. an equation that relates ...

Objective To identify the optimal dose and type of physical activity to improve functional capacity and reduce adverse events in acutely hospitalised older adults. Design Systematic review and ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The early stages of the drug design process involve identifying compounds with ...

In oncology drug development, indication selection and optimal dose identification are the primary objectives for the early phase of clinical trials and could significantly impact the probability of ...