Cancer-causing genes are genes in which mutations cause the onset and spread of cancer. These genes are called driver genes or cancer-causal genes. Several computational methods have been proposed so far to find them. Most of these methods are based on the genome sequencing of cancer tissues. They look for key mutations in genome data to predict cancer genes. This study proposes a new approach called centrality maximization intersection, cMaxDriver, as a NETWORK-based tool for predicting cancer-causing genes in the human transcriptional regulatory NETWORK. In this approach, we used degree, closeness, and betweenness CENTRALITIES, without using genome data. We first constructed three cancer transcriptional regulatory NETWORKs using gene expression data and regulatory interactions as benchmarks. We then calculated the three mentioned CENTRALITIES for the genes in the NETWORK and considered the nodes with the highest values in each of the CENTRALITIES as important genes in the NETWORK. Finally, we identified the nodes with the highest value between at least two CENTRALITIES as cancer causal genes. We compared the results with eighteen previous computational and NETWORK-based methods. The results show that the proposed approach has improved the efficiency and F-measure, significantly. In addition, the cMaxDriver approach has identified unique cancer driver genes, which other methods cannot identify.

In every country, airports are among the most important air transport systems in that country. When an aircraft flies from one airport to another, it creates a graph that can be completed with information about each flight, such as the number of flights per path, the number of passengers, traffic load, and so on. In the present paper, the airports of Iran and the domestic flights are considered as a NETWORK and the structure of the NETWORK is analyzed, and then the measures of complex NETWORKs such as degree distribution, shortest path length, analysis of CENTRALITIES, clustering coefficient and their correlation and the way these CENTRALITIES behave are examined. This analysis shows the Iranian Airport NETWORK (IAN) that has a degree distribution described by the power function. The average path length in this NETWORK is 1. 9, and the average clustering coefficient is 0. 69, which meets the characteristics of a small-world NETWORK and is also considered an example of a disassortative NETWORK. The purpose of this research is to investigate the NETWORK of airports in Iran, which is ultimately important for the expansion of airports, and also to identify the important points of airports.

Introduction: The relations between both rural and urban settlements in the township of Firuzkooh are established based on population flows. These relations have led to the development of some structural NETWORKs and a settlement NETWORK pattern at the local and regional levels. This study already provides a key question on the features of this pattern.Methodology: Data concerning this study have been derived from enquiries of 25 model villages and 436 operational model families of Firuzkooh Township, which are selected, based on Chocran and by considering far and near distances of the villages compared to Firuzkooh town.Results and Discussion: It contains the size of NETWORKs, density and internal / external degree of NETWORK, flow intensity or internal degree of the whole communities enjoyable by each village as the key standards of NETWORK and varieties of CENTRALITIES with commanding standards of a settlement NETWORK. Based on findings, the metropolis Tehran, qualified as a degree centrality, eigenvector centrality and betweenness centrality are known as key settlement as far as the NETWORK structure of Firuzkooh Township is concerned. Again, Mehdishahr, Karkebeneh, Mahmiodabad, Najafdar and Fereydounkenaar qualified as farthermost CENTRALITIES, were identified as the poorest settlements in this NETWORK.Conclusion: Based on study finding, the general pattern governing the settlement NETWORK in Firuzkooh, is a seasonal pattern of population flows in the forms of regular and cyclic one also in the forms of summer and winter NETWORK pattern at local and regional levels. This pattern is in line with the characters of views expressed on growth pole. However, it runs away from NETWORK views patterns. Multiple and directional linkages belonging to rural and urban settlements are owned by settlement NETWORK characters of our area under study.