Prediction of protein functions based on function–function correlation relations

Ng, Ka-Lok; Ciou, Jin-Shuei; Huang, Chien-Hung

doi:10.1016/j.compbiomed.2010.01.001

Cited by 72 publications

(49 citation statements)

References 24 publications

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“…A weighted PPI network can be retrieved from STRING, in which proteins in the network are represented as nodes, while interactions between proteins are given as edges marked with confidence scores if they are in interaction with each other. Interacting proteins with high confidence scores in such a PPI network are more likely to share similar biological functions than noninteractive ones [23–25]. This is because the protein and its interactive neighbours may form a protein complex performing a particular function or may be involved in the same pathway.…”

Section: Methodsmentioning

confidence: 99%

Identification ofInfluenza A/H7N9 Virus Infection-Related Human Genes Based on Shortest Paths in a Virus-Human Protein Interaction Network

Zhang

Jiang

Huang

et al. 2014

BioMed Research International

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The recently emerging Influenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the “guilt by association” rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identify Influenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies.

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Section: Methodsmentioning

confidence: 99%

Identification ofInfluenza A/H7N9 Virus Infection-Related Human Genes Based on Shortest Paths in a Virus-Human Protein Interaction Network

Zhang

Jiang

Huang

et al. 2014

BioMed Research International

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“…The probability function here used to quantize will also help in the guess of treating the patient with unique type [10]. As all know that, cancer will follow a stereotype it will form by of the other possible combinations [8][9]. Search Tool for the Retrieval of Interacting Genes (STRING) is an online database resource that provides both predicted and experimental interaction information with a confidence score.…”

Section: Resultsmentioning

confidence: 99%

Dijkstra’s based identification of lung cancer related genes using PPI networks

Tumuluru¹,

Bhramaramba²

2017

IJCA

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Biomedical area of research has grown dynamically for identification of various diseases and prediction of disease, among the most cancer is vital and critical disease caused from various sources of gene mutation. Cancer is one of the most common diseases in the developed world. Cancer arises from the mutation of a normal gene. Mutated genes that cause cancer are called oncogenes The 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 non small cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lungcancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network using R tool to set the computation. 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. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes identified from the PPI community contained extra most cancers genes than the ones recognized from the gene expression profiles. Furthermore, these genes possessed greater purposeful similarity with the recognized cancer genes than those identified from the gene expression profiles. This work proved the performance of the proposed technique and showed promising consequences.

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“…Several studies have reported that proteins that can interact with each other often share similar functions [22][23][24][25]. This phenomenon implies that a protein that interacts with a protein encoded by a regeneration gene may also be encoded by a regeneration gene.…”

Section: Searching Proceduresmentioning

confidence: 99%

“…Interaction score rule. This rule utilized the fact that proteins that interact with each other can share similar functions [22][23][24][25]. If a candidate gene has strong associations with regeneration genes for two tissues, it is deemed to be a co-regeneration gene with a high probability.…”

Section: Screening Proceduresmentioning

confidence: 99%

Network-Based Method for Identifying Co-Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues

Lei

Pan

Zhang

et al. 2017

Genes

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Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein-protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

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Prediction of protein functions based on function–function correlation relations

Cited by 72 publications

References 24 publications

Identification ofInfluenza A/H7N9 Virus Infection-Related Human Genes Based on Shortest Paths in a Virus-Human Protein Interaction Network

Identification ofInfluenza A/H7N9 Virus Infection-Related Human Genes Based on Shortest Paths in a Virus-Human Protein Interaction Network

Dijkstra’s based identification of lung cancer related genes using PPI networks

Network-Based Method for Identifying Co-Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues

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