Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Novel predictive model of cell survival/death related effects of Extracellular Signal-Regulated kinase protein

Computational modelling is a technique for modelling and solving real-world problems by utilising computing to provide solutions. This paper presents a novel predictive model of cell survival/death-related effects of Extracellular Signal-Regulated Kinase Protein. The computational model was designed using Neural Networks and fuzzy system. Three hundred ERK samples were examined using ten different concentrations of three input proteins: EGF, TNF, and insulin. Based on the different concentrations of input proteins and different samples of ERK protein, adjustment Anderson darling (AD) statistics for multiple distribution functions were computed considering different test such as visual test, Pearson correlation coefficient, and uniformity tests. The results reveal that utilising different concentrations and samples, values such as 7.55 AD and 18.4 AD were obtained using the Weibull distribution function for 0 ng/ml of TNF, 100 ng/ml of EGF, and 0 ng/mL of insulin concentrations. The model was validated by predicting the various ERK protein values that fall within the observed range. The proposed model agrees with the deterministic model, which was developed using difference equations.

Reliability model of the security subsystem countering to the impact of typed cyber-physical attacks

The article's main contribution is the description of the process of the security subsystem countering the impact of typed cyber-physical attacks as a model of end states in continuous time. The input parameters of the model are the flow intensities of typed cyber-physical attacks, the flow intensities of possible cyber-immune reactions, and the set of probabilities of neutralization of cyber-physical attacks. The set of admissible states of the info-communication system is described taking into account possible variants of the development of the modeled process. The initial parameters of the model are the probabilities of the studied system in the appropriate states at a particular moment. The dynamics of the info-communication system's life cycle are embodied in the form of a matrix of transient probabilities. The mentioned matrix connects the initial parameters in the form of a system of Chapman's equations. The article presents a computationally efficient concept based on Gershgorin's theorems to solve such a system of equations with given initiating values. Based on the presented scientific results, the article proposes the concept of calculating the time to failure as an indicator of the reliability of the info-communication system operating under the probable impact of typical cyber-physical attacks. The adequacy of the model and concepts presented in the article is proved by comparing a statically representative amount of empirical and simulated data. We emphasize that the main contribution of the research is the description of the process of the security subsystem countering the impact of typed cyber-physical attacks as a model of end states in continuous time. Based on the created model, the concept of computationally efficient solution of Chapman's equation system based on Gershgorin's theorems and calculating time to failure as an indicator of the reliability of the info-communication system operating under the probable impact of typed cyber-physical attacks are formalized. These models and concepts are the highlights of the research.

Sequential Dihedral Angles (SDAs): A Method for Evaluating the 3D Structure of Proteins

One of the most important steps in modeling three-dimensional (3D) structures of proteins is the evaluation of the constructed models. The present study suggests that the correctness of a structure may be tested by using the characteristics of sequential dihedral angles (SDAs) between adjacent alpha-carbons (C_α) in the main chains of proteins. From our studies on protein structures in the protein data bank (PDB), the SDAs between the C_α in the main chains are limited in their values. In addition, the sum of the absolute values of the three sequential dihedral angles (SDAs) can never be 0 degree. Moreover, 48 degrees is the lowest value existing for the sum of the absolute values of three sequential dihedral angles (SDAs). Thus, the SDAs between the alpha-carbons along the main chains of proteins may be a useful parameter for evaluating anomalies in protein structures.

Investigation of machine learning techniques on proteomics: A comprehensive survey

Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete arrangement of proteins that are created or altered by a life form or framework of the organism. Proteome fluctuates with time and unambiguous prerequisites, or stresses, that a cell or organism experiences. Proteomics is an interdisciplinary area that has derived from the hereditary data of different genome ventures. Much proteomics information is gathered with the assistance of high throughput techniques, for example, mass spectrometry and microarray. It would regularly take weeks or months to analyze the information and perform examinations by hand. Therefore, scholars and scientific experts are teaming up with computer science researchers and mathematicians to make projects and pipeline to computationally examine the protein information. Utilizing bioinformatics procedures, scientists are prepared to do quicker investigation and protein information storing. The goal of this paper is to brief about the review of machine learning procedures and its application in the field of proteomics.

Realizing drug repositioning by adapting a recommendation system to handle the process

Background

Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases.

Results

In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true.

Conclusions

Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

Medical cyber-physical systems: A survey

Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.