The Experts below are selected from a list of 1497 Experts worldwide ranked by ideXlab platform
Kristel Steen - One of the best experts on this subject based on the ideXlab platform.
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A different view on fine-scale population structure in Western African populations
Human Genetics, 2020Co-Authors: Kridsadakorn Chaichoompu, Fentaw Abegaz, Bruno Cavadas, Verónica Fernandes, Bertram Müller-myhsok, Luísa Pereira, Kristel SteenAbstract:Due to its long genetic evolutionary history, Africans exhibit more genetic variation than any other population in the world. Their genetic diversity further lends itself to subdivisions of Africans into groups of individuals with a genetic similarity of varying degrees of granularity. It remains challenging to detect fine-scale structure in a computationally efficient and meaningful way. In this paper, we present a proof-of-concept of a novel fine-scale population structure detection tool with Western African samples. These samples consist of 1396 individuals from 25 ethnic groups (two groups are African American descendants). The strategy is based on a recently developed tool called IPCAPS. IPCAPS, or Iterative Pruning to CApture Population Structure, is a genetic Divisive Clustering strategy that enhances iterative pruning PCA, is robust to outliers and does not require a priori computation of haplotypes. Our strategy identified in total 12 groups and 6 groups were revealed as fine-scale structure detected in the samples from Cameroon, Gambia, Mali, Southwest USA, and Barbados. Our finding helped to explain evolutionary processes in the analyzed West African samples and raise awareness for fine-scale structure resolution when conducting genome-wide association and interaction studies.
Hamid Jafarkhani - One of the best experts on this subject based on the ideXlab platform.
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Energy-Efficient Node Deployment in Heterogeneous Two-Tier Wireless Sensor Networks with Limited Communication Range.
IEEE Transactions on Wireless Communications, 2020Co-Authors: Saeed Karimi-bidhendi, Jun Guo, Hamid JafarkhaniAbstract:We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing Clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.
Alok Sharma - One of the best experts on this subject based on the ideXlab platform.
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Divisive hierarchical maximum likelihood Clustering
BMC Bioinformatics, 2017Co-Authors: Alok Sharma, Yosvany Lopez, Tatsuhiko TsunodaAbstract:Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at Clustering sub-conditions of cancers and other diseases. Hierarchical Clustering methods, which can be categorized into agglomerative and Divisive, have been widely used in such situations. However, unlike agglomerative methods Divisive Clustering approaches have consistently proved to be computationally expensive. The proposed Clustering algorithm (DRAGON) was verified on mutation and microarray data, and was gauged against standard Clustering methods in the literature. Its validation included synthetic and significant biological data. When validated on mixed-lineage leukemia data, DRAGON achieved the highest Clustering accuracy with data of four different dimensions. Consequently, DRAGON outperformed previous methods with 3-,4- and 5-dimensional acute leukemia data. When tested on mutation data, DRAGON achieved the best performance with 2-dimensional information. This work proposes a computationally efficient Divisive hierarchical Clustering method, which can compete equally with agglomerative approaches. The proposed method turned out to correctly cluster data with distinct topologies. A MATLAB implementation can be extraced from http://www.riken.jp/en/research/labs/ims/med_sci_math/ or http://www.alok-ai-lab.com
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Divisive hierarchical maximum likelihood Clustering
'Springer Science and Business Media LLC', 2017Co-Authors: Alok Sharma, Yosvany Lopez, Tatsuhiko TsunodaAbstract:Abstract Background Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at Clustering sub-conditions of cancers and other diseases. Hierarchical Clustering methods, which can be categorized into agglomerative and Divisive, have been widely used in such situations. However, unlike agglomerative methods Divisive Clustering approaches have consistently proved to be computationally expensive. Results The proposed Clustering algorithm (DRAGON) was verified on mutation and microarray data, and was gauged against standard Clustering methods in the literature. Its validation included synthetic and significant biological data. When validated on mixed-lineage leukemia data, DRAGON achieved the highest Clustering accuracy with data of four different dimensions. Consequently, DRAGON outperformed previous methods with 3-,4- and 5-dimensional acute leukemia data. When tested on mutation data, DRAGON achieved the best performance with 2-dimensional information. Conclusions This work proposes a computationally efficient Divisive hierarchical Clustering method, which can compete equally with agglomerative approaches. The proposed method turned out to correctly cluster data with distinct topologies. A MATLAB implementation can be extraced from http://www.riken.jp/en/research/labs/ims/med_sci_math/ or http://www.alok-ai-lab.co
Kridsadakorn Chaichoompu - One of the best experts on this subject based on the ideXlab platform.
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A different view on fine-scale population structure in Western African populations
Human Genetics, 2020Co-Authors: Kridsadakorn Chaichoompu, Fentaw Abegaz, Bruno Cavadas, Verónica Fernandes, Bertram Müller-myhsok, Luísa Pereira, Kristel SteenAbstract:Due to its long genetic evolutionary history, Africans exhibit more genetic variation than any other population in the world. Their genetic diversity further lends itself to subdivisions of Africans into groups of individuals with a genetic similarity of varying degrees of granularity. It remains challenging to detect fine-scale structure in a computationally efficient and meaningful way. In this paper, we present a proof-of-concept of a novel fine-scale population structure detection tool with Western African samples. These samples consist of 1396 individuals from 25 ethnic groups (two groups are African American descendants). The strategy is based on a recently developed tool called IPCAPS. IPCAPS, or Iterative Pruning to CApture Population Structure, is a genetic Divisive Clustering strategy that enhances iterative pruning PCA, is robust to outliers and does not require a priori computation of haplotypes. Our strategy identified in total 12 groups and 6 groups were revealed as fine-scale structure detected in the samples from Cameroon, Gambia, Mali, Southwest USA, and Barbados. Our finding helped to explain evolutionary processes in the analyzed West African samples and raise awareness for fine-scale structure resolution when conducting genome-wide association and interaction studies.
L Billard - One of the best experts on this subject based on the ideXlab platform.
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a study of Divisive Clustering with hausdorff distances for interval data
Pattern Recognition, 2019Co-Authors: Yi Chen, L BillardAbstract:Abstract Clustering methods are becoming key as analysts try to understand what knowledge is buried inside contemporary large data sets. This article analyzes the impact of six different Hausdorff distances on sets of multivariate interval data (where, for each dimension, an interval is defined as an observation [a, b] with a ≤ b and with a and b taking values on the real line R 1 ), used as the basis for Chavent’s [15, 16] Divisive Clustering algorithm. Advantages and disadvantages are summarized for each distance. Comparisons with two other distances for interval data, the Gowda–Diday and Ichino–Yaguchi measures are included. All have specific strengths depending on the type of data present. Global normalization of a distance is not recommended; and care needs to be made when using local normalizations to ensure the features of the underlying data sets are revealed. The study is based on sets of simulated data, and on a real data set.
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dissimilarity measures and Divisive Clustering for symbolic multimodal valued data
Computational Statistics & Data Analysis, 2012Co-Authors: Jaejik Kim, L BillardAbstract:Nowadays, most government agencies and local authorities regularly and routinely collect a large amount of data from censuses and surveys and officially publish them for public purposes. The most frequently used form for the publication is as statistical tables and it is usually not possible to access the raw data for those tables due to privacy issues. Under these situations, we have to analyze data using only those aggregated tables. These tables typically have formats summarized by ordinal or nominal items. Tables for quantitative variables have histogram-valued formats and those for qualitative variables are represented by multimodal-valued types. Both are classes of the so-called symbolic data. In this study, we propose dissimilarity measures and a Divisive Clustering algorithm for symbolic multimodal-valued data. In order to split a partition efficiently at each stage, the algorithm extends the monothetic method for binary data. The proposed method is verified by simulation studies and applied to a work-related nonfatal injury and illness dataset.
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symbolic data analysis conceptual statistics and data mining
2007Co-Authors: L Billard, Edwin DidayAbstract:1. Introduction. References. 2. Symbolic Data. 2.1 Symbolic and Classical Data. 2.2 Categories, Concepts and Symbolic Objects. 2.3 Comparison of Symbolic and Classical Analysis. 3. Basic Descriptive Statistics: One Variate. 3.1 Some Preliminaries. 3.2 Multi-valued Variables. 3.3 Interval-valued Variables. 3.4 Multi-valued Modal variables. 3.5 Interval-valued Modal Variables. 4. Descriptive Statistics: Two or More Variates. 4.1 Multi-valued Variables. 4.2 Interval-valued Variables. 4.3 Modal Multi-valued Variables. 4.4 Modal Interval-valued Variables. 4.5 Baseball Interval-valued Dataset. 4.6 Measures of Dependence. 5. Principal Component Analysis. 5.1 Vertices Method. 5.2 Centers Method. 5.3 Comparison of the Methods. 6. Regression Analysis. 6.1 Classical Multiple Regression Model. 6.2 Multi-valued Variables. 6.3 Interval-valued Variables. 6.4 Histogram-valued Variables. 6.5 Taxonomy Variables. 6.6 Hierarchical Variables. 7. Cluster Analysis. 7.1 Dissimilarity and Distance Measures. 7.2 Clustering Structures. 7.3 Partitions. 7.4 Hierarchy-Divisive Clustering. 7.5 Hierarchy-Pyramid Clusters. Data Index. Author Index. Subject Index.