Dynamical physically structured data modeling vs. classical time series analysis: A case study related to clinical trial data analysis
Abstract
At the time series analysis’s core is the fitting of a preselected model—in the most straightforward case a linear combination of basis functions—to experimental data with one of the performance indices, for example, least squares criterion. However, no matter what the type of such a model is and whatever be the fitting performance index, such an approach does not stipulate for reliance on the understanding those dynamical laws of physics dictating the observable data behavior, given their complexity or uncertainty. Our research looks into a question: “What benefits or advantages could mathematical modeling of such laws be capable of giving when included in nature or experimental data analysis?—nowhere more so than in deciding on life-changing events based on time series analysis in medical practice”.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.