High Accuracy in Silico Sulfotransferase Models

Comparison of murine and human estrogen sulfotransferase 1E1 inhibition in vitro and in silico

Semantically Linking In Silico Cancer Models

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research gro...

emittance control in high power linacs

چکیده این پایان نامه به بررسی اثر سیم پیچ مغناطیسی و کاوه یِ خوشه گر با بسامد رادیویی بر هاله و بیرونگراییِ باریکه هایِ پیوسته و خوشه ایِ ذرات باردار در شتابدهنده های خطیِ یونی، پروتونی با جریان بالا می پردازد و راه حل هایی برای بهینه نگهداشتن این کمیتها ارایه می دهد. بیرونگرایی یکی از کمیتهای اساسی باریکه هایِ ذرات باردار در شتابدهنده ها است که تاثیر قابل توجهی بر قیمت، هزینه و کاراییِ هر شتابدهند...

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellula...

New Approach in Fitting Linear Regression Models with the Aim of Improving Accuracy and Power

The main contribution of this work lies in challenging the common practice of inferential statistics in the realm of simple linear regression for attaining a higher degree of accuracy when multiple observations are available, at least, at one level of the regressor variable. We derive sufficient conditions under which one can improve the accuracy of the interval estimations at quite affordable ...