scholarly journals Sequential Rank Aggregation Method

2018 ◽  
Author(s):  
Yu-ling Lin ◽  
Salem Chakhar ◽  
Rui Yang
Keyword(s):  
Rank Aggregation ◽  
Aggregation Method ◽  
Sequential Rank

Dysregulated microRNAs in laryngeal cancer: a comprehensive meta-analysis using a robust rank aggregation approach

2020 ◽  
Vol 16 (33) ◽  
pp. 2723-2734
Author(s):  
Zaizai Cao ◽  
Yu Guo ◽  
Yinjie Ao ◽  
Shuihong Zhou
Keyword(s):  
Target Genes ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Rank Aggregation ◽  
The Cancer Genome Atlas ◽  
Aggregation Method ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Genome Atlas

We need a reasonable method of compiling data from different studies regarding the expression of microRNA (miRNA) in laryngeal squamous cell carcinoma (LSCC). The robust rank aggregation method was used to integrate the rank lists of miRNAs from 11 studies. The enrichment analysis was performed on target genes of meta-signature miRNAs. The Cancer Genome Atlas database was used to confirm the results of meta-analysis. Three meta-signature miRNAs (miR-21-5p, miR-196a-5p and miR-145-5p) were obtained. All three miRNAs could be prognostic for LSCC. The enrichment analysis showed that these miRNAs were associated significantly with multiple cancer-related signaling pathways. The robust rank aggregation approach is an effective way to identify important miRNAs from different studies. All identified miRNAs could be candidates for LSCC diagnostic and prognostic biomarkers.

A New Method to Obtain a Consensus Ranking of a Region's Vintages’ Quality

2012 ◽  
Vol 7 (1) ◽  
pp. 88-107 ◽  
Author(s):  
José Borges ◽  
António C. Real ◽  
J. Sarsfield Cabral ◽  
Gregory V. Jones
Keyword(s):  
Software Tool ◽  
Production Quality ◽  
Jel Classification ◽  
Rank Aggregation ◽  
Aggregation Method ◽  
Wine Quality ◽  
Wine Production ◽  
Rating Format ◽  
Relative Quality

AbstractAn impartial assessment of the quality of the wine produced over the years in a region (vintage quality) is an essential tool for producers, consumers, investors, and wine researchers to understand factors influencing quality and make purchasing or investing decisions. However, scoring the overall wine quality over the years does not necessarily produce a consensus of which year or years are best. Several critics, magazines, and organizations publish vintage charts that assign a score to each vintage, representing the corresponding perception of the wine quality. Often, the scores given by different institutions reveal little consensus with respect to the relative quality of the vintages.In this work, we propose the utilization of a rank aggregation method to combine a collection of vintage charts for a region into a ranking of the vintages that represents the consensus of the input vintage charts. As a result, we obtain an impartial ranking of the vintages that represents the consensus of an arbitrary number of independent vintage charts. We illustrate the method with the scores from three wine regions.The proposed method produces a ranking of vintage-to-vintage quality that represents an impartial consensus of a collection of independent sources, each using a different rating format, scale, or classification. Such a ranking has the potential to be useful for the research community, which needs a relative measure of wine production quality over the years. Therefore, we make publicly available a software tool that implements the method (Borges, 2011). (JEL Classification: C38, C61, C88)

Evaluation of Researchers' Academic Influence Based on Rank Aggregation Method

2021 ◽  
Author(s):  
Jingbo Huang ◽  
Feng Yao ◽  
Tao Wang ◽  
Jiting Li ◽  
Dayong Shen ◽  
...  
Keyword(s):  
Rank Aggregation ◽  
Aggregation Method ◽  
Academic Influence

scholarly journals Development and validation of a transcriptional signature for the assessment of fibrosis in organs

2020 ◽  
Author(s):  
Bin Wang ◽  
Shiju Chen ◽  
Hongyan Qian ◽  
Rongjuan Chen ◽  
Yan He ◽  
...  
Keyword(s):  
Severity Score ◽  
Expression Profiling ◽  
Critical Factor ◽  
Rank Aggregation ◽  
Integrated Analysis ◽  
Transcriptional Level ◽  
Variation Analysis ◽  
Aggregation Method ◽  
Transcriptional Signature ◽  
Development And Validation

AbstractBackgroundFibrosis in most organs has proven to be an critical factor related to high risk of morbidity and mortality, but an adequate assessment of fibrosis severity is still challenging. This study tried to evaluate fibrosis severity through a fibrosis transcriptional signature.MethodsA fibrosis transcriptional signature was developed through an integrated analysis of multiple expression profiling datasets of human organs with fibrosis-related diseases. A fibrosis severity score for each sample was the calculated through gene set variation analysis (GSVA), and its role in evaluating fibrosis severity was then analyzed.ResultsTen expression profiling datasets of human tissues with organ failure were integrated with robust rank aggregation method, and a fibrosis severity score consisting of 149 genes. Most of those included genes were involved in fibrogenic pathways. GSEA analysis revealed that fibrosis transcriptional signature was significantly enriched in the fibrogenic tissues. Additionally, we found that fibrosis transcriptional signature could effectively differentiate fibrosis tissues and non-fibrosis tissues.ConclusionThis study developed an useful fibrosis transcriptional signature involved in fibrosis-related diseases. This fibrosis transcriptional signature is helpful in precisely evaluating the fibrosis severity in common organs at the transcriptional level.

Web Service Ranking using Rank Aggregation Method

2017 ◽  
Vol 7 (4) ◽  
pp. 293-297
Author(s):  
Ashwini Chavhan ◽  
◽  
R. R. Badre ◽  
M. A. Kulkarni ◽  
◽  
...  
Keyword(s):  
Web Service ◽  
Rank Aggregation ◽  
Aggregation Method ◽  
Service Ranking

An Ensemble Matchers Based Rank Aggregation Method for Taxonomy Matching

2015 ◽  
pp. 190-202 ◽  
Author(s):  
Hailun Lin ◽  
Yuanzhuo Wang ◽  
Yantao Jia ◽  
Jinhua Xiong ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Rank Aggregation ◽  
Aggregation Method
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