scholarly journals Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance

2021 ◽  
Author(s):  
Nadia Tahiri ◽  
Bernard Fichet ◽  
Vladimir Makarenkov
Keyword(s):  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Clustering Algorithm ◽  
Heterogeneous Cluster ◽  
Consensus Tree ◽  
Evolutionary Patterns ◽  
Consensus Trees ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Additive Trees

AbstractEach gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree that accounts for the main patterns of vertical descent. The output of traditional consensus tree or supertree inference methods is a unique consensus tree or supertree. Here, we describe a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of phylogenetic trees (i.e. additive trees or X-trees). We show how a specific version of the popular k-means clustering algorithm, based on some interesting properties of the Robinson and Foulds topological distance, can be used to partition a given set of trees into one (when the data are homogeneous) or multiple (when the data are heterogeneous) cluster(s) of trees. We adapt the popular Caliński-Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with k-means. A special attention is paid to the relevant but very challenging problem of inferring alternative supertrees, built from phylogenies constructed for different, but mutually overlapping, sets of taxa. The use of the Euclidean approximation in the objective function of the method makes it faster than the existing tree clustering techniques, and thus perfectly suitable for the analysis of large genomic datasets. In this study, we apply it to discover alternative supertrees characterizing the main patterns of evolution of SARS-CoV-2 and the related betacoronaviruses.

scholarly journals A deep learning approach for building multiple trees classification

2019 ◽  
Author(s):  
Nadia Tahiri
Keyword(s):  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Consensus Methods ◽  
Heterogeneous Cluster ◽  
Consensus Tree ◽  
Evolutionary Patterns ◽  
History Of ◽  
Additive Trees ◽  
Multiple Trees ◽  
Overlapping Sets

Each gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer or hybridization events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree, or Tree of Life, that represents the main patterns of vertical descent. Here, we present a new efficient method for inferring single or multiple consensus trees and supertrees for a given set of phylogenetic trees (i.e. additive trees or X-trees). The output of the traditional tree consensus methods is a unique consensus tree or supertree. Here, we show how Machine Learning (ML) models, based on some interesting properties of the Robinson and Foulds topological distance, can be used to partition a given set of trees into one (when the data are homogeneous) or multiple (when the data are heterogeneous) cluster(s) of trees. We adapt the popular Accuracy, Precision, Sensitivity, and F1 scores to the tree clustering. A special attention is paid to the relevant, but very challenging, problem of inferring alternative supertrees that are built from phylogenies defined on different, but mutually overlapping, sets of species. The use of an approximate objective function in clustering makes the new method faster than the existing tree clustering techniques and thus suitable for the analysis of large genomic datasets.

scholarly journals Design of an Unsupervised Machine Learning-Based Movie Recommender System

2020 ◽  
Author(s):  
Debby Cintia Ganesha Putri ◽  
Jenq-Shiou Leu ◽  
Pavel Seda
Keyword(s):  
Recommender System ◽  
Clustering Algorithm ◽  
System Development ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Computational Time ◽  
Agglomerative Clustering ◽  
Method Performance ◽  
Cluster Validity Indices ◽  
Validity Indices

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and, Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used Average Similarity, Computational Time, Association Rule with Apriori algorithm, and Clustering Performance Evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies-Bouldin Index.

scholarly journals MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Mengni Liu ◽  
Jianyu Chen ◽  
Xin Wang ◽  
Chengwei Wang ◽  
Xiaolong Zhang ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Cancer Evolution ◽  
Evolutionary Patterns ◽  
Evolutionary Trajectory ◽  
Wide Range ◽  
Heterogeneous Features ◽  
History Of ◽  
Somatic Alterations ◽  
Tumor Biopsies

Abstract Background Multi-region sequencing (MRS) has been widely used to analyze intra-tumor heterogeneity (ITH) and cancer evolution. However, comprehensive analysis of mutational data from MRS is still challenging, necessitating complicated integration of a plethora of computational and statistical approaches. Findings Here, we present MesKit, an R/Bioconductor package that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS. MesKit provides a wide range of analysis and visualization modules, including ITH evaluation, metastatic route inference, and mutational signature identification. In addition, MesKit implements an auto-layout algorithm to generate phylogenetic trees based on somatic mutations. The application of MesKit for 2 reported MRS datasets of hepatocellular carcinoma and colorectal cancer identified known heterogeneous features and evolutionary patterns, together with potential driver events during cancer evolution. Conclusions In summary, MesKit is useful for interpreting ITH and tracing evolutionary trajectory based on MRS data. MesKit is implemented in R and available at https://bioconductor.org/packages/MesKit under the GPL v3 license.

scholarly journals Design of an Unsupervised Machine Learning-Based Movie Recommender System

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 185 ◽  
Author(s):  
Debby Cintia Ganesha Putri ◽  
Jenq-Shiou Leu ◽  
Pavel Seda
Keyword(s):  
Recommender System ◽  
Clustering Algorithm ◽  
System Development ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Computational Time ◽  
Agglomerative Clustering ◽  
Method Performance ◽  
Cluster Validity Indices ◽  
Validity Indices

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies–Bouldin Index.

scholarly journals Clustering Algorithms and Validation Indices for a Wide mmWave Spectrum

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 287 ◽  
Author(s):  
Bogdan Antonescu ◽  
Miead Tehrani Moayyed ◽  
Stefano Basagni
Keyword(s):  
Communication Systems ◽  
Clustering Algorithm ◽  
Radio Channel ◽  
Clustering Algorithms ◽  
Wireless Communication Systems ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Wide Range ◽  
Radio Signals ◽  
Urban Scenario

Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters provides better spatial and temporal characteristics of the mmWave channel. This paper deals with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. By way of simulations, we show that in outdoor communication scenarios clustering of received rays is influenced by the frequency of the transmitted signal. This demonstrates the sparse characteristic of the mmWave spectrum (i.e., we obtain a lower number of rays at the receiver for the same urban scenario). We use the well-known k-means clustering algorithm to group arriving rays at the receiver. The accuracy of this partitioning is studied with both cluster validity indices (CVIs) and score fusion techniques. Finally, we analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas.

scholarly journals Role of Cluster Validity Indices in Delineation of Precipitation Regions

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1372
Author(s):  
Nikhil Bhatia ◽  
Jency M. Sojan ◽  
Slobodon Simonovic ◽  
Roshan Srivastav
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Ratio Test ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Point Data ◽  
Optimal Number Of Clusters

The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W − 1 B ) indices found to be the best for all seasons in both the regions.

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