Zeitschrift für Informatik und Systembiologie

Objectives: Illustration of features of big data which make the Vehicular Ad Hoc Network communication more accurate and precise. The open framework like Hadoop and Map Reduce which are used in big data for managing, storing and accessing of information is also provided.

Methods/Statistical analysis: The technology of big data is continuously growing and with its rapid increase it is gaining the attention of the researchers. The data is analyzed and outputted in a form that it helps in making quick responses and action in real time environment like Vehicular Ad hoc network. Big data helps in gaining the insight view of the stored, operational and altered data, to improve the traffic conditions. When the Vehicular Ad Hoc Network and the big data are combined, it helps in maintaining the large amount of traffic triggers very easily as the data mining process in big data helps to make quick decisions on the basis of statistics or graph, which are the result of analysis of data.

Findings: Big data and Hadoop cannot be compared because these two are reciprocal to each other. Big data can be considered as a problem and Hadoop can be a solution to it. The combination of Hadoop and Big data in Vehicular Ad Hoc Network provides services useful for number of applications.

Application/Improvements: A lot of applications can be made in future which helps in making the big data analysis much easier and helps in making the on-road condition more secure.

The continuously rising cost of medical spending, population size is growing up and increasing their need for healthcare, which requires time saving for laboratory technicians at the examination is a great incentive to create a new approach that helps to provide health care to patients at lower costs with good management. We started to apply the concept of the autonomic system that let the system work without intervention of the user. It has given by implemented and designed Case-Based Reasoning (CBR) algorithm in suitable way to self diagnose diabetes for patients depending on some tests results. The result is implementing new self diagnosis of diabetes system without user intervention which suggests that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.

Cancer is a system with thousands of genes and proteins with the complex interactions between them. By examining the cancer drug activity on only part of this system, we do not know in which direction the whole system will evolve, and whether therapy will be useful or not. This is one of the main reasons why cancer therapies still do not meet our expectations. In order to find more effective anticancer therapies, it is important to consider the impact of drugs on the entire cancer system.

The second largest eigenvalue plays a key role in complex systems optimization. The algorithms minimizing the second largest eigenvalue of graphs have been already used to speed up processes in computer networks and differential cryptanalysis. Based on the aforementioned, it could be assumed that maximizing the second highest eigenvalue could slow down the processes in metabolic networks that describe processes in cancer. To verify our hypothesis, we have built the in silico model of cancer Vini and run it on a supercomputer. Vini transformed the metabolic pathways of cancer from Kyoto Encyclopedia of Genes and Genomes into the binding energy matrices representing binding energies between the genes and proteins on one side and drugs being investigated on another side. Some matrix elements also represent interactions between proteins and genes. Then, it calculated the second largest eigenvalues of these matrices.

In the end, we compared the calculated results against the existing in vitro and in vivo experimental results. The calculated efficacy of cancer drugs was confirmed in 79.31% of in vivo experimental cases, and in 92.30% of in vitro experimental cases.

These results show that the second largest eigenvalue plays an important role in metabolic cancer networks and that the Vini model can be an effective aid in finding more effective cancer therapies.