Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality

In vivo Expansion of Naïve CD4+CD25high FOXP3+ Regulatory T Cells in Patients with Colorectal Carcinoma after IL-2 Administration

Regulatory T cells (Treg cells) are increased in context of malignancies and their expansion can be correlated with higher disease burden and decreased survival. Initially, interleukin 2 (IL-2) has been used as T-cell growth factor in clinical vaccination trials. In murine models, however, a role of IL-2 in development, differentiation, homeostasis, and function of Treg cells was established. In IL-2 treated cancer patients a further Treg-cell expansion was described, yet, the mechanism of expansion is still elusive. Here we report that functional Treg cells of a naïve phenotype - as determined by CCR7 and CD45RA expression - are significantly expanded in colorectal cancer patients. Treatment of 15 UICC stage IV colorectal cancer patients with IL-2 in a phase I/II peptide vaccination trial further enlarges the already increased naïve Treg-cell pool. Higher frequencies of T-cell receptor excision circles in naïve Treg cells indicate IL-2 dependent thymic generation of naïve Treg cells as a mechanism leading to increased frequencies of Treg cells post IL-2 treatment in cancer patients. This finding could be confirmed in naïve murine Treg cells after IL-2 administration. These results point to a more complex regulation of Treg cells in context of IL-2 administration. Future strategies therefore might aim at combining IL-2 therapy with novel strategies to circumvent expansion and differentiation of naïve Treg cells

Microenvironmental Influences that Drive Progression from Benign Breast Disease to Invasive Breast Cancer

Invasive breast cancer represents the endpoint of a developmental process that originates in the terminal duct lobular units and is believed to progress through stages of increasing proliferation, atypical hyperplasia, and carcinoma in situ before the cancer acquires invasive and metastatic capabilities. By comparison with invasive breast cancer, which has been studied extensively, the preceding stages of benign breast disease are more poorly understood. Much less is known about the molecular changes underlying benign breast disease development and progression, as well as the transition from in situ into invasive disease. Even less focus has been given to the specific role of stroma in this progression. The reasons for lack of knowledge about these lesions often come from their small size and limited sample availability. More challenges are posed by limitations of the models used to investigate the lesions preceding invasive breast cancer. However, recent studies have identified alterations in stromal cell function that may be critical for disease progression from benign disease to invasive cancer: key functions of myoepithelial cells that maintain tissue structure are lost, while tissue fibroblasts become activated to produce proteases that degrade the extracellular matrix and trigger the invasive cellular phenotype. Gene expression profiling of stromal alterations associated with disease progression has also identified key transcriptional changes that occur early in disease development. In this review, we will summarize recent studies showing how stromal factors can facilitate progression of ductal carcinoma in situ to invasive disease. We also suggest approaches to identify processes that control earlier stages of disease progression

User preferences in Bayesian multi-objective optimization: the expected weighted hypervolume improvement criterion

To be published in the proceedings of LOD 2018 – The Fourth International Conference on Machine Learning, Optimization, and Data Science – September 13-16, 2018 – Volterra, Tuscany, ItalyIn this article, we present a framework for taking into account user preferences in multi-objective Bayesian optimization in the case where the objectives are expensive-to-evaluate black-box functions. A novel expected improvement criterion to be used within Bayesian optimization algorithms is introduced. This criterion, which we call the expected weighted hypervolume improvement (EWHI) criterion, is a generalization of the popular expected hypervolume improvement to the case where the hypervolume of the dominated region is defined using an absolutely continuous measure instead of the Lebesgue measure. The EWHI criterion takes the form of an integral for which no closed form expression exists in the general case. To deal with its computation, we propose an importance sampling approximation method. A sampling density that is optimal for the computation of the EWHI for a predefined set of points is crafted and a sequential Monte-Carlo (SMC) approach is used to obtain a sample approximately distributed from this density. The ability of the criterion to produce optimization strategies oriented by user preferences is demonstrated on a simple bi-objective test problem in the cases of a preference for one objective and of a preference for certain regions of the Pareto front