Some extensions of the precise consistency consensus matrix

2015 ◽  
Vol 74 ◽  
pp. 67-77 ◽  
Author(s):  
María Teresa Escobar ◽  
Juan Aguarón ◽  
José María Moreno-Jiménez
Keyword(s):  
Consensus Matrix

A Decision Support System and Visualisation Tools for AHP-GDM

2021 ◽  
pp. 703-722
Author(s):  
Alberto Turón ◽  
Juan Aguarón ◽  
María Teresa Escobar ◽  
José María Moreno-Jiménez
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Group Decision Making ◽  
Support System ◽  
Pairwise Comparison ◽  
Decision Makers ◽  
Consensus Matrix ◽  
Minimum Number

The Precise Consistency Consensus Matrix (PCCM) is a decisional tool for AHP-Group Decision Making (AHP-GDM). Based on the initial pairwise comparison matrices of the individuals, the PCCM constructs a consensus matrix for the group using the concept of consistency. This paper presents a decision support system (PRIOR-PCCM) that facilitates the construction of the PCCM in the context of AHP-GDM, and the calculus of four indicators that allows comparison of the behaviour of group consensus matrices. PRIOR-PCCM incorporates the possibility of considering different weights for the decision makers and includes a module that permits the extension of the initial PCCM which can achieve the minimum number of non-null entries required for deriving priorities or establishing a complete PCCM matrix. It also includes two cardinal indicators for measuring consistency and compatibility and two ordinal indicators for evaluating the number of violations of consistency and priority. The paper introduces some new visualisation tools that improve comprehension of the process followed for obtaining the PCCM matrix and allow the cognitive exploitation of the results. These original contributions are illustrated with a case study.

scholarly journals AHP-Group Decision Making Based on Consistency

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 242 ◽  
Author(s):  
Juan Aguarón ◽  
María Teresa Escobar ◽  
José Moreno-Jiménez ◽  
Alberto Turón
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Real Life ◽  
Decision Makers ◽  
Local Context ◽  
Consensus Matrix ◽  
Stability Intervals ◽  
The Individual ◽  
Individual Consistency

The Precise consistency consensus matrix (PCCM) is a consensus matrix for AHP-group decision making in which the value of each entry belongs, simultaneously, to all the individual consistency stability intervals. This new consensus matrix has shown significantly better behaviour with regards to consistency than other group consensus matrices, but it is slightly worse in terms of compatibility, understood as the discrepancy between the individual positions and the collective position that synthesises them. This paper includes an iterative algorithm for improving the compatibility of the PCCM. The sequence followed to modify the judgments of the PCCM is given by the entries that most contribute to the overall compatibility of the group. The procedure is illustrated by means of its application to a real-life situation (a local context) with three decision makers and four alternatives. The paper also offers, for the first time in the scientific literature, a detailed explanation of the process followed to solve the optimisation problem proposed for the consideration of different weights for the decision makers in the calculation of the PCCM.

The precise consistency consensus matrix in a local AHP-group decision making context

2014 ◽  
Vol 245 (1-2) ◽  
pp. 245-259 ◽  
Author(s):  
Juan Aguarón ◽  
María Teresa Escobar ◽  
José María Moreno-Jiménez
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Consensus Matrix

Constant community discovery in network based on consensus clustering

2014 ◽  
Vol 28 (19) ◽  
pp. 1450126
Author(s):  
Zongwen Liang ◽  
Athina Petropulu ◽  
Fan Yang ◽  
Jianping Li
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Consensus Clustering ◽  
Community Discovery ◽  
Structural And Functional Properties ◽  
Consensus Matrix ◽  
Internal Properties ◽  
Community Detection Algorithm ◽  
Vertex Order

Community detection is a fundamental work to analyze the structural and functional properties of complex networks. There are many algorithms proposed to find the optimal communities of network. In this paper, we focus on how vertex order influences the results of community detection. By using consensus clustering, we discover communities and get a consensus matrix under different vertex orders. Based on the consensus matrix, we study the phenomenon that some nodes are always allocated in the same community even with different vertex permutations. We call this group of nodes as constant community and propose a constant community detection algorithm (CCDA) to find constant communities in network. We also further study the internal properties of constant communities and find constant communities play a guiding role in community detection. Finally, a discussion of constant communities is given in the hope of being useful to others working in this field.

scholarly journals Finding Clusters of Similar-minded People on Twitter Regarding the Covid-19 Pandemic

2021 ◽  
Author(s):  
Philipp Kappus ◽  
Paul Groß
Keyword(s):  
Feature Vector ◽  
Public Debate ◽  
The Other ◽  
Fake News ◽  
Clustering Methods ◽  
New Approach ◽  
Public Opinions ◽  
Consensus Matrix

Two clustering methods to determine users with similar opinions on the Covid-19 pandemic and the related public debate in Germany will be presented in this paper. We believe, they can helpgaining an overview over similar-minded groups and could support the prevention of fake-news distribution. The first method uses a new approach to create a network based on retweetrelationships between users and the most retweeted users, the so-called influencers. The second method extracts hashtags from users posts to create a “user feature vector” which is then clustered, using a consensus matrix based on previous work, to identify groups using the same language. With both approaches it was possible to identify clusters that seem to fit groups of different public opinions in Germany. However, we also found that clusters from one approach cannot be associated with clusters from the other due to filtering steps in the two methods.

Local multiple alignment by consensus matrix

1992 ◽  
Vol 8 (4) ◽  
pp. 339-345
Author(s):  
Nickolai N. Alexandrov
Keyword(s):  
Multiple Alignment ◽  
Consensus Matrix

scholarly journals Splicing Enhancers at Intron–Exon Borders Participate in Acceptor Splice Sites Recognition

2020 ◽  
Vol 21 (18) ◽  
pp. 6553
Author(s):  
Tatiana Kováčová ◽  
Přemysl Souček ◽  
Pavla Hujová ◽  
Tomáš Freiberger ◽  
Lucie Grodecká
Keyword(s):  
Splice Site ◽  
Messenger Rna ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Affinity Purification ◽  
Genetic Diagnostics ◽  
Dual Function ◽  
Small Nuclear Ribonucleoprotein ◽  
Consensus Matrix ◽  
Site Recognition

Acceptor splice site recognition (3′ splice site: 3′ss) is a fundamental step in precursor messenger RNA (pre-mRNA) splicing. Generally, the U2 small nuclear ribonucleoprotein (snRNP) auxiliary factor (U2AF) heterodimer recognizes the 3′ss, of which U2AF35 has a dual function: (i) It binds to the intron–exon border of some 3′ss and (ii) mediates enhancer-binding splicing activators’ interactions with the spliceosome. Alternative mechanisms for 3′ss recognition have been suggested, yet they are still not thoroughly understood. Here, we analyzed 3′ss recognition where the intron–exon border is bound by a ubiquitous splicing regulator SRSF1. Using the minigene analysis of two model exons and their mutants, BRCA2 exon 12 and VARS2 exon 17, we showed that the exon inclusion correlated much better with the predicted SRSF1 affinity than 3′ss quality, which were assessed using the Catalog of Inferred Sequence Binding Preferences of RNA binding proteins (CISBP-RNA) database and maximum entropy algorithm (MaxEnt) predictor and the U2AF35 consensus matrix, respectively. RNA affinity purification proved SRSF1 binding to the model 3′ss. On the other hand, knockdown experiments revealed that U2AF35 also plays a role in these exons’ inclusion. Most probably, both factors stochastically bind the 3′ss, supporting exon recognition, more apparently in VARS2 exon 17. Identifying splicing activators as 3′ss recognition factors is crucial for both a basic understanding of splicing regulation and human genetic diagnostics when assessing variants’ effects on splicing.

A Decision Support System and Visualisation Tools for AHP-GDM

2019 ◽  
Vol 11 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Alberto Turón ◽  
Juan Aguarón ◽  
María Teresa Escobar ◽  
José María Moreno-Jiménez
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Group Decision ◽  
Pairwise Comparison ◽  
Decision Makers ◽  
Group Consensus ◽  
Consensus Matrix ◽  
Minimum Number

The Precise Consistency Consensus Matrix (PCCM) is a decisional tool for AHP-Group Decision Making (AHP-GDM). Based on the initial pairwise comparison matrices of the individuals, the PCCM constructs a consensus matrix for the group using the concept of consistency. This paper presents a decision support system (PRIOR-PCCM) that facilitates the construction of the PCCM in the context of AHP-GDM, and the calculus of four indicators that allows comparison of the behaviour of group consensus matrices. PRIOR-PCCM incorporates the possibility of considering different weights for the decision makers and includes a module that permits the extension of the initial PCCM which can achieve the minimum number of non-null entries required for deriving priorities or establishing a complete PCCM matrix. It also includes two cardinal indicators for measuring consistency and compatibility and two ordinal indicators for evaluating the number of violations of consistency and priority. The paper introduces some new visualisation tools that improve comprehension of the process followed for obtaining the PCCM matrix and allow the cognitive exploitation of the results. These original contributions are illustrated with a case study.

scholarly journals Sc-GPE: A Graph Partitioning-Based Cluster Ensemble Method for Single-Cell

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaoshu Zhu ◽  
Jian Li ◽  
Hong-Dong Li ◽  
Miao Xie ◽  
Jianxin Wang
Keyword(s):  
Single Cell ◽  
Graph Partitioning ◽  
Biological Significance ◽  
Ensemble Method ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Cluster Ensemble ◽  
Ensemble Strategy ◽  
Consensus Matrix ◽  
The Individual

Clustering is an efficient way to analyze single-cell RNA sequencing data. It is commonly used to identify cell types, which can help in understanding cell differentiation processes. However, different clustering results can be obtained from different single-cell clustering methods, sometimes including conflicting conclusions, and biologists will often fail to get the right clustering results and interpret the biological significance. The cluster ensemble strategy can be an effective solution for the problem. As the graph partitioning-based clustering methods are good at clustering single-cell, we developed Sc-GPE, a novel cluster ensemble method combining five single-cell graph partitioning-based clustering methods. The five methods are SNN-cliq, PhenoGraph, SC3, SSNN-Louvain, and MPGS-Louvain. In Sc-GPE, a consensus matrix is constructed based on the five clustering solutions by calculating the probability that the cell pairs are divided into the same cluster. It solved the problem in the hypergraph-based ensemble approach, including the different cluster labels that were assigned in the individual clustering method, and it was difficult to find the corresponding cluster labels across all methods. Then, to distinguish the different importance of each method in a clustering ensemble, a weighted consensus matrix was constructed by designing an importance score strategy. Finally, hierarchical clustering was performed on the weighted consensus matrix to cluster cells. To evaluate the performance, we compared Sc-GPE with the individual clustering methods and the state-of-the-art SAME-clustering on 12 single-cell RNA-seq datasets. The results show that Sc-GPE obtained the best average performance, and achieved the highest NMI and ARI value in five datasets.

scholarly journals TraRECo: A Greedy Approach based de novo Transcriptome Assembler with Read Error Correction using Consensus Matrix

2017 ◽  
Author(s):  
Seokhyun Yoon ◽  
Daeseung Kim ◽  
Keunsoo Kang ◽  
Woong June Park
Keyword(s):  
Information Quality ◽  
De Novo ◽  
Read Depth ◽  
De Bruijn Graph ◽  
Depth Information ◽  
Greedy Approach ◽  
Read Alignment ◽  
Consensus Matrix ◽  
Path Search ◽  
De Bruijn

AbstractBackgroundChallenges in developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize de Bruijn graph, which has a complexity linearly growing with data size while suffers from errors and repeat. Although one can correct errors by inspecting topological structure of the graph, it is an uneasy task when there are too many branches. There are two research directions: improving either graph reliability or path search precision. We focused on improving the reliability.ResultsWe present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. The idea is similar to overlap-layout-consensus approach used for genome assembly, but is different in that consensus is made through the entire graph construction step. Basically, we built contigs by direct read alignment within a distance margin and performed junction search to construct splicing graphs. While doing so, however, a contig of length l was represented by 4×1 matrix (called consensus matrix), of which each element was the base count of aligned reads so far. A representative sequence is obtained, by taking majority in each column of the consensus matrix, to be used for further read alignment. Once splicing graphs were obtained, we used IsoLasso to find paths with noticeable read depth. The experiments using real and simulated reads showed that the method provides considerable improvements in sensitivity and reasonably better performances when comparing both sensitivity and precision. This could be achieved by making more erroneous reads to be participated in graph construction, which, in turn, improved the depth information quality used for the subsequent path search step. The results for simulated reads showed also challenges are still remaining since non-negligible percentage of transcripts with high abundance were not recovered by the assemblers we considered.Conclusionde novo assembly is mainly to explore not-yet-discovered isoforms and must be able to represent as much reads as possible in an efficient way. In this sense, TraRECo provides us a potential alternative to improve graph reliability, even though the computational burden can be much higher than single k-mer de Bruijn graph approach.

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