A proposed voting scheme to reduce the sensitivity of the Markov method

Vaziri, Baback; Yih, Yuehwern; Morin, Tom

doi:10.1504/ijor.2018.091200

Cited by 3 publications

(4 citation statements)

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“…The predictive power of popular sports ranking methods in the… perturbed data (in the sports world, think of this as upsets), especially in its tail (Chartier et al 2011a;Vaziri et al 2018b). The Markov method is known to exhibit faulty behavior under certain conditions (Chartier et al 2011a;Vaziri et al 2018a, b), and can result in teams having the incentive to lose a match to increase their rating!…”

Section: Markov Methodsmentioning

confidence: 99%

“…In well-connected graphs, the user can choose a sufficiently large value for (for example, = 1000 ). In these cases, the ranking vector will converge to that of the traditional Markov method, but it will remove potential issues such as a periodic Markov chain or the need for an adjustment in the event of a perfect season (Vaziri et al 2018b). In poorly-connected Markov graphs, the sensitivity and stability of the ranking vector is compromised (Chartier et al 2011a;Vaziri et al 2018b) and a smaller value of is needed.…”

Section: Choosingmentioning

confidence: 99%

“…Of the five evaluated ranking methods, each of them violated at least one of the listed properties for a fair and comprehensive method. The authors then proposed a modification to the Markov method, called the (1, ) method (Vaziri et al 2018b), and show that under certain parametric conditions, this method will satisfy all three properties.…”

Section: Introductionmentioning

confidence: 99%

“…There is additional literature that evaluates various ranking methods and examines their predictive power and performance (Barrow et al 2013;Burer 2012;Chartier et al 2011a;Langville and Meyer 2012;Vaziri et al 2018b). Ultimately, however, the performance of a ranking method will be relative to the attributes that it strives to evaluate (Keeney and Gregory 2005).…”

Section: Introductionmentioning

confidence: 99%

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The predictive power of popular sports ranking methods in the NFL, NBA, and NHL

Dabadghao

Vaziri

2021

Oper Res Int J

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Section: Markov Methodsmentioning

confidence: 99%

Section: Choosingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The predictive power of popular sports ranking methods in the NFL, NBA, and NHL

Dabadghao

Vaziri

2021

Oper Res Int J

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Heterogeneous data analysis of hypertrophic cardiomyopathy to prioritize important genes

Janyasupab

Suratanee

Plaimas

2022

2022 26th International Computer Science and Engineering Conference (ICSEC)

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GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data

Janyasupab,

Suratanee,

Plaimas

2023

PeerJ Computer Science

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Background Identifying the genes responsible for diseases requires precise prioritization of significant genes. Gene expression analysis enables differentiation between gene expressions in disease and normal samples. Increasing the number of high-quality samples enhances the strength of evidence regarding gene involvement in diseases. This process has led to the discovery of disease biomarkers through the collection of diverse gene expression data. Methods This study presents GeneCompete, a web-based tool that integrates gene expression data from multiple platforms and experiments to identify the most promising biomarkers. GeneCompete incorporates a novel union strategy and eight well-established ranking methods, including Win-Loss, Massey, Colley, Keener, Elo, Markov, PageRank, and Bi-directional PageRank algorithms, to prioritize genes across multiple gene expression datasets. Each gene in the competition is assigned a score based on log-fold change values, and significant genes are determined as winners. Results We tested the tool on the expression datasets of Hypertrophic cardiomyopathy (HCM) and the datasets from Microarray Quality Control (MAQC) project, which include both microarray and RNA-Sequencing techniques. The results demonstrate that all ranking scores have more power to predict new occurrence datasets than the classical method. Moreover, the PageRank method with a union strategy delivers the best performance for both up-regulated and down-regulated genes. Furthermore, the top-ranking genes exhibit a strong association with the disease. For MAQC, the two-sides ranking score shows a high relationship with TaqMan validation set in all log-fold change thresholds. Conclusion GeneCompete is a powerful web-based tool that revolutionizes the identification of disease-causing genes through the integration of gene expression data from multiple platforms and experiments.

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A proposed voting scheme to reduce the sensitivity of the Markov method

Cited by 3 publications

References 0 publications

The predictive power of popular sports ranking methods in the NFL, NBA, and NHL

The predictive power of popular sports ranking methods in the NFL, NBA, and NHL

Heterogeneous data analysis of hypertrophic cardiomyopathy to prioritize important genes

GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data

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