Cancer subtype prediction from a pathway-level perspective by using a support vector machine based on integrated gene expression and protein network

Hung, Fei-Hung; Chiu, Hung Wen

doi:10.1016/j.cmpb.2017.01.006

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…The experimental study gave good results with a mean AUC value of 80% when integrated genomic and radiology images were taken. On the other side, Hung and Chui [33] used scored equation [34] which integrates gene expression and protein network datasets to identify subtypes of cancer correctly with the help of SVM [35] classifier. SVM attains 70% accuracy by accurately predicting three subtypes of the tumours.…”

Section: Introductionmentioning

confidence: 99%

eBreCaP: extreme learning‐based model for breast cancer survival prediction

Dhillon

Singh

2020

IET syst. biol.

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Section: Introductionmentioning

confidence: 99%

eBreCaP: extreme learning‐based model for breast cancer survival prediction

Dhillon

Singh

2020

IET syst. biol.

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“…To utilize the interaction effect between genes across multi-omics data, network-based integrative approaches have several advantages, such as utilizing the interrelationships among multi-omics data, better biological interpretation, and improved outcome prediction power, as shown in many studies (Jeong et al, 2015;Kim et al, 2015a;Lee et al, 2019;Vangimalla et al, 2016;Wang et al, 2017;Kim et al, 2017). To effectively combine different types of genomic features on the graph, most network-based integrative methods have focused on incorporating prior knowledge such as pathway or subtype information in many cancer studies (Hung and Chiu, 2017;Liu et al, 2015;Dimitrakopoulos et al, Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

Kim

Choe

Shivakumar

et al. 2020

Preprint

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MotivationTo better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks.ResultsAs a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets.AvailabilityiDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.

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“…SVM is a supervised learning model commonly used in machine learning, proposed by Cortes and Vapnik in 1995 [10]. Early diagnosis and prognosis of cancer have become a necessary condition for cancer research because they can promote subsequent clinical management of patients.…”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels

Liu

Dong

et al. 2020

BioMed Research International

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Objective. Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy. Methods. Microarray datasets related with FAP were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes were identified by FAP vs. normal samples and FAP and duodenal cancer vs. normal samples. Furthermore, functional enrichment analyses of these differentially expressed genes were performed. A support vector machine (SVM) was performed to train and validate cancer risk prediction model. Results. A total of 196 differentially expressed genes were identified between FAP compared with normal samples. 177 similarly expressed genes were identified both in FAP and duodenal cancer, which were mainly enriched in pathways in cancer and metabolic-related pathway, indicating that these genes in patients with FAP could contribute to duodenal cancer. Among them, Cyclin D1, SDF-1, AXIN, and TCF were significantly upregulated in FAP tissues using qRT-PCR. Based on the 177 genes, an SVM model was constructed for prediction of the risk of cancer in patients with FAP. After validation, the model can accurately distinguish FAP patients with high risk from those with low risk for duodenal cancer. Conclusion. This study proposed a cancer risk prediction model based on an SVM at the transcript levels.

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Cancer subtype prediction from a pathway-level perspective by using a support vector machine based on integrated gene expression and protein network

Cited by 18 publications

References 30 publications

eBreCaP: extreme learning‐based model for breast cancer survival prediction

eBreCaP: extreme learning‐based model for breast cancer survival prediction

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels

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