Protein homeostasis is a dynamic network that plays a pivotal role in systems' maintenance within a cell. This quality control system involves a number of mechanisms regarding the process of protein folding. Chaperones play a critical role in the folding, refolding, and unfolding of proteins. Aggregation of misfolded proteins is a common characteristic of neurodegenerative diseases. Chaperones act in a variety of pathways in this critical interplay between protein homeostasis network and misfolded protein's load. Moreover, ER stress-induced cell death mechanisms (such as the unfolded protein response) are activated as a response. Therefore, there is a critical balance in the accumulation of misfolded proteins and ER stress response mechanisms which can lead to cell death. Our focus is in understanding the different mechanisms that govern ER stress signaling in health and disease in order to represent the regulation of protein homeostasis and balance of protein synthesis and degradation in the ER. Our proposed model describes, using hybrid modeling, the function of chaperones' machinery for protein folding.This paper describes a comprehensive application with three features to diagnose common and rare diseases and to optimize health and vitality of the user with the help of broad databases and state-of-the-art technologies. The features comprise 1. A digital blood analysis for optimizing the patient's metabolism and health. Individual workout plans are made for the patient's individual body and their individual injuries. Individual meal recommendations are made depending on blood count, needed calories, physical activities, and food allergies of the patient. A digital anamnesis is included in this app. Medical information is digitally stored and made useful for further health-related purposes. 2. A digital and automated diagnosis application that can diagnose more than 7000 common and rare diseases. 3. A food guidance application which provides diet recommendations for the patients based on their physical constitution, physical health aims, allergies, and daily needed calories.Nowadays, cancer constitutes the second leading cause of death globally. The application of an efficient classification model is considered essential in modern diagnostic medicine in order to assist experts and physicians to make more accurate and early predictions and reduce the rate of mortality. Machine learning techniques are being broadly utilized for the development of intelligent computational systems, exploiting the recent advances in digital technologies and the significant storage capabilities of electronic media. Ensemble learning algorithms and semi-supervised algorithms have been independently developed to build efficient and robust classification models from different perspectives. The former attempts to achieve strong generalization by using multiple learners, while the latter attempts to achieve strong generalization by exploiting unlabeled data. In this work, we propose an improved semi-supervised self-labeled algorithm for cancer prediction, based on ensemble methodologies. Our preliminary numerical experiments illustrate the efficacy and efficiency of the proposed algorithm, proving that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.In recent years, a highly sophisticated array of modeling and simulation tools in all areas of biological and biomedical research has been developed. These tools have the potential to provide new insights into biological mechanisms integrating subcellular, cellular, tissue, organ, and potentially whole organism levels. Current research is focused on how to use these methods for translational medical research, such as for disease diagnosis and understanding, as well as drug discovery. In addition, these approaches enhance the ability to use human-derived data and to contribute to the refinement of high-cost experimental-based research. Additionally, the conflicting conceptual frameworks and conceptions of modeling and simulation methods from the broad public of users could have a significant impact on the successful implementation of aforementioned applications. This in turn could result in successful collaborations across academic, clinical, and industrial sectors. To that end, this study provides an overview of the frameworks and disciplines used for validation of computational methodologies in biomedical sciences.Prisoners' Dilemma is a well-known game in game theory with many variations and applications in a number of fields. The addition of quantum strategies in this game opens up new possibilities and changes the equilibria of the game significantly.Motivation In the last years, systems-level network-based approaches have gained ground in the research field of systems biology. These approaches are based on the analysis of high-throughput sequencing studies, which are rapidly increasing year by year.  https://www.selleckchem.com/products/azd3229.html  Nowadays, the single-cell RNA-sequencing, an optimized next-generation sequencing (NGS) technology that offers a better understanding of the function of an individual cell in the context of its microenvironment, prevails. Results Toward this direction, a method is developed in which active molecular subpathways are recorded during the time evolution of the disease under study. This method operates for expression profiling by high-throughput sequencing data. Its capability is based on capturing the temporal changes of local gene communities that form a disease-perturbed subpathway. The aforementioned methods are applied to real data from a recent study that uses single-cell RNA-sequencing data related with the progression of neurodegeneration. More specific, microglia cells were isolated from the hippocampus of a mouse model with Alzheimer's disease-like phenotypes and severe neurodegeneration and of control mice at multiple time points during progression of neurodegeneration. Our analysis offers a different view for neurodegeneration progression under the perspective of systems biology. Conclusion Our approach into the molecular perspective using a temporal tracking of active pathways in neurodegeneration at single-cell resolution may offer new insights for designing new efficient strategies to treat Alzheimer's and other neurodegenerative diseases.