Identification of compound–protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds

Lei, Chen; Zhang, Yuhang; Zheng, Mingyue; Huang, Tao; Cai, Yu-Dong

doi:10.1007/s00438-016-1240-x

Cited by 58 publications

(48 citation statements)

References 89 publications

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“…Therefore, candidate genes that had similar associations with uveitis GO terms and KEGG pathways were more likely to be novel uveitis-related genes. We performed GO term (KEGG pathway) [123,124,125,126] enrichment analysis for candidate genes and uveitis-related genes. The representation of a gene g on all GO terms and KEGG pathways was encoded into a vector ES (g) using this theory.…”

Section: Methodsmentioning

confidence: 99%

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network

Yan

et al. 2017

IJMS

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Uveitis, defined as inflammation of the uveal tract, may cause blindness in both young and middle-aged people. Approximately 10–15% of blindness in the West is caused by uveitis. Therefore, a comprehensive investigation to determine the disease pathogenesis is urgent, as it will thus be possible to design effective treatments. Identification of the disease genes that cause uveitis is an important requirement to achieve this goal. To begin to answer this question, in this study, a computational method was proposed to identify novel uveitis-related genes. This method was executed on a large protein–protein interaction network and employed a popular ranking algorithm, the Random Walk with Restart (RWR) algorithm. To improve the utility of the method, a permutation test and a procedure for selecting core genes were added, which helped to exclude false discoveries and select the most important candidate genes. The five-fold cross-validation was adopted to evaluate the method, yielding the average F1-measure of 0.189. In addition, we compared our method with a classic GBA-based method to further indicate its utility. Based on our method, 56 putative genes were chosen for further assessment. We have determined that several of these genes (e.g., CCL4, Jun, and MMP9) are likely to be important for the pathogenesis of uveitis.

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Section: Methodsmentioning

confidence: 99%

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network

Yan

et al. 2017

IJMS

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“…To analyze them, a reliable and widely used feature selection method; i.e., the mRMR method [ 27 ], was employed in this study. This method has been widely applied to analyze several complicated biological problems [ 35 , 37 – 44 ]. Two excellent criteria were used in this method: maximum relevance and minimum redundancy.…”

Section: Methodsmentioning

confidence: 99%

“…MCC is more proper because it is a balanced measurement even if the sizes of positive and negative samples have a great difference. Furthermore, it has been used as the major measurement in several studies [ 35 , 37 , 54 – 57 ]. Thus, it was selected as the major method to evaluate the predicted results yielded by different prediction models.…”

Section: Methodsmentioning

confidence: 99%

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

et al. 2017

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Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

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“…If a candidate gene exhibits a similar relationship with GO terms and KEGG pathways to those of MD-related genes, it is more likely to be a novel MD-related gene. According to the enrichment theory of GO terms and KEGG pathways [ 30 , 31 , 71 , 72 ], the relationship between a gene g and GO terms or KEGG pathways can be encoded as a numeric vector, denoted by FV ( g ). The proximity of two genes g and g ′ on GO terms and KEGG pathways can be measured by the direction cosine of vectors FV (g) and FV ( g ′), which can be formulated as …”

Section: Methodsmentioning

confidence: 99%

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

Wang

et al. 2017

PLoS ONE

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As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

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Identification of compound–protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds

Cited by 58 publications

References 89 publications

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

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