An integrated method for the identification of novel genes related to oral cancer

Lei, Chen; Yang, Jing; Xing, Zhihao; Yuan, Fei; Shu, Yang; Zhang, Yunhua; Kong, Xiangyin; Huang, Tao; Li, Haipeng; Cai, Yu-Dong

doi:10.1371/journal.pone.0175185

Cited by 20 publications

(15 citation statements)

References 137 publications

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“…In detail, for a feature set, say F i = {f 1 ,f 2 ,…,f i }, containing the top i features in F , all samples are represented by the features in F i . Then, a classification algorithm is executed on these samples with its performance evaluated by one of the cross-validation methods [ 122 – 128 ]. After all of the possible feature sets have been tested, the feature set yielding the best performance can be found.…”

Section: Methodsmentioning

confidence: 99%

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

et al. 2017

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Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.

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Section: Methodsmentioning

confidence: 99%

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

et al. 2017

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“…With the development of bioinformatics, some computational methods have been presented to contribute to the identification of similar disease associated genes [ 28 ] and other related problems [ 29 – 31 ]. Among them, a network-based method is an important type of computational method [ 32 – 37 ], such as Guilt-by-association (GBA)-based methods [ 38 – 40 ], the random walk with restart (RWR) algorithm [ 41 – 43 ], and the shortest path algorithm [ 43 – 51 ]. This study also built a network-based method to identify novel MD-related genes.…”

Section: Introductionmentioning

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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“…Recently, the classical shortest path graph algorithm has been widely adopted to identify genes with novel functions [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. According to this method, the shortest paths connecting any two validated genes were calculated in a constructed network, and the inner nodes (genes) of these paths were extracted as candidate genes.…”

Section: Introductionmentioning

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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An integrated method for the identification of novel genes related to oral cancer

Cited by 20 publications

References 137 publications

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

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

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

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