Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies. Alzheimer is an incurable neurological disorder and is classified as an aging disease. It is one of the important neurological complications which can affect the whole society ranging from the patients themselves to the people who are around them. The aging population is growing in many countries, and the treatment costs of Alzheimer are dramatically high. These issues have drawn the attention of many researchers to the importance of the examination of this disease 1. There are many organizations all over the world which work in the field of early diagnosis and prevention of Alzheimer 2,3. National center for health statistics considers Alzheimer's disease as the sixth cause of death in the United States 4. As a result, Alzheimer's disease is among the costliest diseases for various socioeconomic classes. As the population of the world grows, the number of inflicted people increases. Therefore, the control of the affected population becomes more difficult 5. Significant advances in medical and neurological sciences have led to a longer life expectancy and have increased the number of Alzheimer's disease patients. Ultimately, the prevention of disease before its occurrence is regarded to be one of the most important pillars of treatment at different stages of this disease. Treatment or postponement of a disease depends on its discovery by identifying the biological pathways involved in the disease and adopting various drug-disease network approaches 6 to control these pathways. In recent decades, deep investigation of molecular mechanisms has become more prevalent as a researc...

Alzheimer’s disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer’s disease, its prevention and treatment are vital. This study proposes a method to extract substantial gene complexes and then introduces potential drugs in Alzheimer’s disease. To this end, a protein-protein interaction (PPI) network was utilized to extract five meaningful gene complexes functionally interconnected. An enrichment analysis to introduce the most important biological processes and pathways was accomplished on the obtained genes. The next step is extracting the drugs related to AD and introducing some new drugs which may be helpful for this disease. Finally, a complete network including all the genes associated with each gene complex group and genes’ target drug was illustrated. For validating the proposed potential drugs, Connectivity Map (CMAP) analysis was accomplished to determine target genes that are up- or downregulated by proposed drugs. Medical studies and publications were analyzed thoroughly to introduce AD-related drugs. This analysis proves the accuracy of the proposed method in this study. Then, new drugs were introduced that can be experimentally examined as future work. Raloxifene and gentian violet are two new drugs, which have not been introduced as AD-related drugs in previous scientific and medical studies, recommended by the method of this study. Besides the primary goal, five bipartite networks representing the genes of each group and their target miRNAs were constructed to introduce target miRNAs.

Background: Alzheimer's disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer’s disease, its prevention and treatment is vital. This study proposes a method to extract substantial gene complexes and accomplish an enrichment analysis to introduce the most significant biological procedures. The next step is extracting the drugs related to AD and introduce some new drugs which may be useful for this disease. Results: To this end, protein-protein interactions (PPI) network was utilized to extract five meaningful gene complexes functionally interconnected. The next step was to construct a five bipartite network representing the genes of each group and their target miRNAs. Finally, a complete network including all the genes related to each gene complex group and genes’ target drug was illustrated. medical studies and publications were analyzed thoroughly to introduce AD-related drugs. Conclusions: This analysis proves the accuracy of the proposed method in this study. Then, new drugs were introduced that can be experimentally examined as future work. RALOXIFENE, GENTIAN VIOLET are two new drugs, which have not been introduced as AD-related drugs in previous scientific and medical studies, recommended by the method of this study. These two drugs.

Here we present an application of a supervised feed forward artificial neural network (ANN) that is trained on the basis of genetic algorithm (GA). The network model is used for predicting the magnitude of earthquakes in the North Tabriz Fault (NTF) Northwest Iran. The earthquake database was derived from the catalogues of both the International Institute of Earthquake Engineering and Seismicity of Iran and the Iranian Seismological Center. For this purpose, three temporal seismicity parameters were calculated using the ZMAP MATLAB toolbox. The performance of the artificial neural network (ANN) model was measured in terms of accuracy by a tenfold crossvalidation as 99.11%. Another evaluation method was predicting a case event that occurred on 11 August 2012 in Ahar-Varzeghan in Iran. Results showed that the ANN optimized with GA (ANNGA) learning optimization model is suitable and may be useful for predicting future earthquakes, especially in active seismologic regions.

No abstract

Active faults are sources of earthquakes and one of them is north fault of Tabriz in the northwest of Iran. The activation of faults can harm humans' life and constructions. The analysis of the seismic data in active regions can be helpful in dealing with earthquake hazards and devising prevention strategies. In this chapter, structure of earthquake events along with application of various intelligent data mining algorithms for earthquake prediction are studied. Main focus is on categorizing the seismic data of local regions according to the events' location using clustering algorithms for classification and then using intelligent artificial neural network for cluster prediction. As a result, the target data were clustered to six groups and proposed model with 10 fold cross validation yielded accuracy of 98.3%. Also, as a case study, the tectonic stress on concentration zones of Tabriz fault has been identified and five features of the events were used. Finally, the most important points have been proposed for evaluation of the nonlinear model predictions as future directions.