scholarly journals A Graphic Method for Identification of Novel Glioma Related Genes

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yu-Fei Gao ◽  
Yang Shu ◽  
Lei Yang ◽  
Yi-Chun He ◽  
Li-Peng Li ◽  
...  
Keyword(s):  
Shortest Path ◽  
Protein Interaction ◽  
Biological Process ◽  
Weighted Graph ◽  
Intracranial Tumor ◽  
Graphic Method ◽  
Shortest Path Algorithm ◽  
Protein Protein Interaction ◽  
Serious Disease ◽  
Effective Treatments

Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.

scholarly journals Identifying Gastric Cancer Related Genes Using the Shortest Path Algorithm and Protein-Protein Interaction Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yang Jiang ◽  
Yang Shu ◽  
Ying Shi ◽  
Li-Peng Li ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
High Probability ◽  
Biological Process ◽  
Interaction Network ◽  
Shortest Path Algorithm ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Gastric cancer, as one of the leading causes of cancer related deaths worldwide, causes about 800,000 deaths per year. Up to now, the mechanism underlying this disease is still not totally uncovered. Identification of related genes of this disease is an important step which can help to understand the mechanism underlying this disease, thereby designing effective treatments. In this study, some novel gastric cancer related genes were discovered based on the knowledge of known gastric cancer related ones. These genes were searched by applying the shortest path algorithm in protein-protein interaction network. The analysis results suggest that some of them are indeed involved in the biological process of gastric cancer, which indicates that they are the actual gastric cancer related genes with high probability. It is hopeful that the findings in this study may help promote the study of this disease and the methods can provide new insights to study various diseases.

scholarly journals Identification of Age-Related Macular Degeneration Related Genes by Applying Shortest Path Algorithm in Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jian Zhang ◽  
Min Jiang ◽  
Fei Yuan ◽  
Kai-Yan Feng ◽  
Yu-Dong Cai ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Shortest Path ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Age Related Macular Degeneration ◽  
Shortest Path Algorithm ◽  
Protein Protein Interaction ◽  
Age Related ◽  
Protein Protein Interaction Network

This study attempted to find novel age-related macular degeneration (AMD) related genes based on 36 known AMD genes. The well-known shortest path algorithm, Dijkstra’s algorithm, was applied to find the shortest path connecting each pair of known AMD related genes in protein-protein interaction (PPI) network. The genes occurring in any shortest path were considered as candidate AMD related genes. As a result, 125 novel AMD genes were predicted. The further analysis based on betweenness and permutation test indicates that there are 10 genes involved in the formation or development of AMD and may be the actual AMD related genes with high probability. We hope that this contribution would promote the study of age-related macular degeneration and discovery of novel effective treatments.

scholarly journals Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Lei Chen ◽  
Jian Zhang ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Shortest Paths ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Ppi Network ◽  
Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

scholarly journals Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baoman Wang ◽  
Fei Yuan ◽  
Xiangyin Kong ◽  
Lan-Dian Hu ◽  
Yu-Dong Cai
Keyword(s):  
Cell Death ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Weighted Graph ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

scholarly journals A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins

2019 ◽  
Author(s):  
Stavros Makrodimitris ◽  
Marcel Reinders ◽  
Roeland van Ham
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Process ◽  
Poor Performance ◽  
Protein Interaction Data ◽  
Physical Interaction ◽  
Cellular Functions ◽  
Data Resource ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractPhysical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extened these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.

scholarly journals Computational Identification of Guillain-Barre Syndrome Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network

2021 ◽  
Author(s):  
Chunyang Wang ◽  
Shiwei Liao ◽  
jing xu
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Shortest Path ◽  
Protein Interaction ◽  
Guillain Barre Syndrome ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Guillain Barré Syndrome ◽  
Guillain Barré

Abstract In this study, we developed a computational method to identify Guillain–Barré syndrome (GBS) related genes based on (i) a gene expression profile, and (ii) the shortest path analysis in a protein-protein interaction (PPI) network. The mRMR (Maximum Relevance Minimum Redundancy) method was employed to select significant genes from an mRNA profile dataset of GBS patients and healthy controls. The protein products of the significant genes were then mapped to a PPI network generated from the STRING database. Shortest paths were computed and all shortest path proteins were picked out and were ranked according to their betweenness. Related genes of the top-most proteins in the ordered list were then retrieved and were regarded as potential GBS related genes in this study. As a result, totally 30 GBS related genes were screened out, in which 20 were retrieved from PPI analysis of up-regulated expressed genes and 23 were from down-regulated expressed genes (13 overlap genes). GO enrichment and KEGG enrichment analysis were performed respectively. Results showed that there were some overlap GO terms and KEGG pathway terms in both up-regulated and down-regulated analysis, which indicated these terms may play critical role during GBS process. These results could shed some light on the understanding of the Genetic and molecular pathogenesis of GBS disease, providing basis for future experimental biology studies and for the development of effective genetic strategies for GBS clinical therapies.

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