Aim: The aim of this study is to investigate the PROTEIN-PROTEIN Interaction NETWORK of Celiac Disease. Background: Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The PROTEIN interaction NETWORK is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. Material and methods: In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate PROTEINs were selected for this study. The NETWORKs of related differentially expressed PROTEIN were explored using Cytoscape 3. 3 and the PPI analysis methods such as MCODE and ClueGO. Results: According to the NETWORK analysis Ubiquitin C, Heat shock PROTEIN 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa PROTEIN, and PROTEIN 5 (glucose-regulated PROTEIN, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks PROTEINs. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed PROTEINs. Conclusion: Chaperons have a bold presentation in curtail area in NETWORK therefore these key PROTEINs beside the other hubbottlneck PROTEINs may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.

Aim: Gene assessment of pancreatic adenocarcinoma disease via PROTEIN-PROTEIN interaction (PPI) NETWORK Analysis. Background: Diagnosis, especially early detection of pancreatic adenocarcinoma as a lethal disease implies more investigation. PPI NETWORK Analysis is a suitable tool to discover new aspects of molecular mechanism of diseases. Methods: In the present study the related genes to pancreatic adenocarcinoma are studied in the interactome unit and the key genes are highlighted. The significant clusters were introduced by Cluster-ONE application of Cytoscape software 3. 4. 0. The genes are retrieved from STRING date base and analyzed by Cytoscape software. The crucial genes based on analysis of central parameters were determined and enriched by ClueGO v2. 3. 5 via gene ontology. Results: The number of 24 key genes among 794 initial genes were highlighted as crucial nodes in relationship with pancreatic adenocarcinoma. All of the key genes were organized in a cluster including 216 nodes. The main related pathways and cancer diseases were determined. Conclusion: It was concluded that the introduced 24 genes are possible biomarker panel of pancreatic adenocarcinoma.

Background: Dynamic PROTEIN-PROTEIN interaction NETWORKs (DPPIN) can confirm the conditional and temporal features of PROTEINs and PROTEIN complexes. In addition, the relation of PROTEIN complexes in dynamic NETWORKs can provide useful information in understanding the dynamic functionality of PPI NETWORKs. Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset. Material and Methods: In this analytical study, the static PROTEIN-PROTEIN interaction NETWORK is transformed into a dynamic NETWORK using the gene expression thresholding to extract the PROTEIN complex relations. The number of presented PROTEINs of the dynamic NETWORK is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard PROTEIN complexes as reference PROTEIN complexes, the number of items decreases from active PROTEINs to PROTEIN complexes at each transaction. Extracted sub graphs as PROTEIN complexes, at each time point, are weighted according to the reference PROTEIN complexes similarity degrees. Mega-transactions and extended items are created based on occurrence bitmap matrix of the reference complexes. Rules will be extracted based on Mega-transactions of PROTEIN complexes. Results: The proposed method has been evaluated using gold standard PROTEIN complex rules. The amount of extracted rules from Biogrid datasets and PROTEIN complexes are 281, with support 0. 2. Conclusion: The characteristic of the proposed algorithm is the simultaneous extraction of intra-transaction and inter-transaction rules. The results evaluation using EBI data shows the efficiency of the proposed algorithm.

Background: Gliosarcoma (GS) is a rare primary neoplasm of the central nervous system. It is a subtype of glioblastoma and has a biphasic pattern consisting of glial and malignant mesenchymal elements. Its onset is between the fourth and sixth decade of life. Objectives: Since PROTEIN-PROTEIN interaction (PPI) NETWORK analysis can provide useful information about molecular aspects of diseases, the aim of this study is GS PROTEIN analysis via PPI NETWORK and gene ontology assessment. Methods: The related genes to GS were gathered from STRING DB and organized in the interacted NETWORK by Cytoscape software version 3. 6. 0. The NETWORK was analyzed based on topological parameters and the central nodes were introduced. The significant clusters were identified by ClusterONE and the cluster included more key genes enriched via gene ontology by ClueGO. Results: Nine crucial genes including TP53, EGFR, PTEN, EGR1, VEGFA, HSP90AA1, IL2, KNG1, and HSP90AB1 were introduced as related key genes to GS. Two significant clusters contain most of central genes. Twenty-one elements of cluster-1, which included 7 key genes, were enriched via gen ontology and 115 related terms were determined and discussed. Conclusions: The nine introduced central genes may play main roles in pathology of GS. However, experimental investigation is proposed to validate the findings.

Aim: Evaluation of biological characteristics of 13 identified PROTEINs of patients with cirrhotic liver disease is the main aim of this research.Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task.Methods: Essential analysis, such as gene ontology (GO) enrichment and PROTEIN-PROTEIN interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query.Results: Based on GO analysis, most of PROTEINs are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. PROTEIN-PROTEIN interaction NETWORK analysis introduced five PROTEINs (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding PROTEIN and apolipoPROTEIN A-I) as hub and bottleneck PROTEINs.Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned PROTEINs will provide a better understanding of cirrhosis disease.

A biological NETWORK represents the interaction between a set of macromolecules to drive a particular biological process. In a biological environment, abnormalities happen not only in one molecule but also through a biological NETWORK. One of the most effective methods to detect anomaly is the comparison between healthy and diseased NETWORKs. In this regard, biological NETWORK alignment is one of the most efficient ways to find the difference between healthy and diseased cells. This problem, PROTEIN-PROTEIN interaction NETWORK alignment, has been raised in two main types: Local NETWORK alignment and Global NETWORK alignment. According to the NP-completeness of this problem, different non-deterministic approaches have been proposed to tackle the Global NETWORK alignment problem. Recently, NetAl has been introduced as a common algorithm to align two NETWORKs. Although this algorithm can align two NETWORKs at the appropriate time, it does not consider biological features. In this study, we present a new framework called PRAF to improve the results of NETWORK alignment algorithms such as NetAl by considering some biological features like gene ontology (GO).