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Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity

STATISTICS IN MEDICINE 2024年第6期43卷 1135-1152页

作者： Cremaschi, Andrea De Iorio, Maria Kothandaraman, Narasimhan Yap, Fabian Tint, Mya Thway Eriksson, Johan ASTAR Singapore Inst Clin Sci Singapore Singapore Natl Univ Singapore Yong Loo Lin Sch Med Singapore Singapore UCL Dept Stat Sci London England KK Womens & Childrens Hosp Dept Paediat Singapore Singapore ASTAR Singapore Inst Clin Sci 30 Med Dr Singapore 117609 Singapore

The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

关键词： Dirichlet process Gaussian process graph-based clustering graphical models longitudinal data metabolomics

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Comparative karyotype analysis among six species of Ipomoea based on two newly identified repetitive sequences

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GENOME 2019年第4期62卷 243-252页

作者： Sun, Jianying Yu, Lixuan Cai, Zeixi Zhang, An Jin, Weiwei Han, Yonghua Li, Zongyun Jiangsu Normal Univ Sch Life Sci Inst Integrat Plant Biol Xuzhou Jiangsu Peoples R China Jiangsu Normal Univ Jiangsu Key Lab Phylogen & Comparat Genom Xuzhou Jiangsu Peoples R China China Agr Univ Natl Maize Improvement Ctr China Coordinated Res Ctr Crop Biol Beijing Key Lab Crop Genet Improvement Beijing Peoples R China Chinese Acad Agr Sci Jiangsu Xuhuai Reg Xuzhou Inst Agr Sci Sweetpotato Res Inst Xuzhou Jiangsu Peoples R China

Sweet potato is one of the most important crops worldwide;however, basic research in this crop is limited. In this study, we aimed to construct a detailed karyotype of six species of Ipomoea (hexaploid Ipomoea batatas and five related species, namely, one tetraploid, I. tabascana and four diploids, I. splendor-sylvae, I. trifida, I. tenuissima, and I. x leucantha) and understand the relationship among these species. Two satellite repeats (viz., Itf_1 and Itf_2) were identified from the diploid I. trifida genome sequence using RepeatExplorer on Galaxy. Together with the ribosomal DNA (rDNA), although without distinguishable chromosomes, a detailed karyotype was constructed for the six species. Our results showed a similar karyotype between I. tenuissima and I. x leucantha, indicating their close relationship. The signal distribution pattern of Itf_1, 45S rDNA combination, detected only in I. trifida, I. tabascana, and I. batatas, implied their close relationships. The chromosomes carrying 5S rDNA could be conserved among the six species as they always carried the Itf_2 signals, which generated a similar signal distribution pattern. The results enabled a detailed comparative cytogenetic analysis, providing valuable information to understand the relationship among these species and help assemble the genome sequence of the six species of Ipomoea.

关键词： graph-based clustering karyotype fluorescence in situ hybridization repetitive sequence series Batatas

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Recognition of group patterns in geological maps by building similarity networks

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GEOCARTO INTERNATIONAL 2022年第2期37卷 607-626页

作者： Sayidov, A. Weibel, R. Leyk, S. Univ Zurich Dept Geog Zurich Switzerland Univ Colorado Dept Geog Boulder CO 80309 USA

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps.

关键词： Cartographic pattern detection map generalization geological mapping graph-based clustering similarity network

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Including crystallographic symmetry in quantum-based refinement: Q|R#2

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ACTA CRYSTALLOgraphICA SECTION D-STRUCTURAL BIOLOGY 2020年第1期76卷 41-50页

作者： Zheng, Min Biczysko, Malgorzata Xu, Yanting Moriarty, Nigel W. Kruse, Holger Urzhumtsev, Alexandre Waller, Mark P. Afonine, Pavel, V Shanghai Univ Int Ctr Quantum & Mol Struct Shanghai 200444 Peoples R China Westfalische Wilhelms Univ Munster Theoret Organ Chem Organ Chem Inst D-48149 Munster Germany Westfalische Wilhelms Univ Munster Ctr Multiscale Theory & Computat D-48149 Munster Germany Lawrence Berkeley Natl Lab Mol Biosci & Integrated Bioimaging Berkeley CA 94720 USA Czech Acad Sci Inst Biophys Kralovopolska 135 Brno 61265 Czech Republic CNRS INSERM UdS Inst Genet & Biol Mol & Cellulaire 1 Rue Laurent FriesBP 10142 F-67404 Illkirch Graffenstaden France Univ Lorraine Fac Sci & Technol BP 239 F-54506 Vandoeuvre Les Nancy France Pending AI Pty Ltd iAccelerate Innovat CampusSquires Way North Wollongong NSW 2500 Australia

Three-dimensional structure models refined using low-resolution data from crystallographic or electron cryo-microscopy experiments can benefit from high-quality restraints derived from quantum-chemical methods. However, nonperiodic atom-centered quantum-chemistry codes do not inherently account for nearest-neighbor interactions of crystallographic symmetry-related copies in a satisfactory way. Here, these nearest-neighbor effects have been included in the model by expanding to a super-cell and then truncating the super-cell to only include residues from neighboring cells that are interacting with the asymmetric unit. In this way, the fragmentation approach can adequately and efficiently include nearest-neighbor effects. It has previously been shown that a moderately sized X-ray structure can be treated using quantum methods if a fragmentation approach is applied. In this study, a target protein (PDB entry 4gif) was partitioned into a number of large fragments. The use of large fragments (typically hundreds of atoms) is tractable when a GPU-based package such as TeraChem is employed or cheaper (semi-empirical) methods are used. The QM calculations were run at the HF-D3/6-31G level. The models refined using a recently developed semi-empirical method (GFN2-xTB) were compared and contrasted. To validate the refinement procedure for a non-P1 structure, a standard set of crystallographic metrics were used. The robustness of the implementation is shown by refining 13 additional protein models across multiple space groups and a summary of the refinement metrics is presented.

关键词： quantum refinement symmetry fragmentation graph-based clustering crystallography cryo-EM Hartree-Fock theory

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Representative operating scenario selection with algebraic multi-grid clustering for integrated energy systems planning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年 146卷

作者： Mozafari, Yasaman Rosehart, W. D. Univ Calgary Schulich Sch Engn Elect & Software Engn Dept Calgary AB Canada

Recent shifts towards higher renewable penetration in power systems, has resulted in increasing gas-fired generation capacity in systems without hydro or other fast responding alternatives, implying increased electricity and gas infrastructure interdependency. Furthermore, highly efficient combined heat and power (CHP) units lead to heat and electricity interdependency. It is crucial to consider these interdependencies in the expansion planning of energy systems for an effective and reliable investment planning and policy design. In this paper, the problem of integrated expansion planning of electricity, heat, and gas in presence of demand and wind uncertainty is addressed. Representative operating scenario selection with algebraic multi -grid clustering is used to model wind and demand uncertainties, and to address computational complexity of a central planning model. The simulation results on a modified IEEE-118 bus test system with a 14-node gas network shows that the algebraic multi-grid clustering outperforms the classical methods such as kmeans by following the benchmark case more closely.

关键词： Integrated planning Representative scenario selection graph-based clustering Algebraic-multi grid clustering

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A geospatial clustering algorithm and its integration into a techno-economic rural electrification planning model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期137卷

作者： Torres-Perez, Mirelys Dominguez, Javier Arribas, Luis Amador, Julio Ciller, Pedro Gonzalez-Garcia, Andres Univ Las Tunas Dept Informat Las Tunas Cuba CIEMAT Renewable Energies Div Ave Complutense40 Madrid 28040 Spain Univ Politecn Madrid Dept Elect Engn Madrid 28012 Spain Univ Pontificia Comillas IIT Comillas Inst Invest Tecnol Madrid 28015 Spain Massachusetts Inst Technol CSAIL MIT Cambridge MA 02139 USA

Rural electrification planning is a complex process requiring careful consideration of various factors to ensure efficient and cost-effective solutions. Existing clustering methods in academic literature often fall short in this context, as they typically do not account for geographical barriers, restricted areas, and key electrical and geospatial metrics simultaneously. This can result in clusters that do not meet the energy needs of the study region, potentially causing inefficient energy distribution and increased costs. This study presents a novel clustering algorithm, RElect_MGEC (Rural Electrification Microgrid and Grid Extension clustering), specifically designed for techno-economic planning in rural areas. The RElect_MGEC algorithm combines density-based and graph clustering methods to group households while considering constraints imposed by geographic barriers, electricity power, and distance from the generation center. The algorithm was implemented within the IntiGIS (Geographic Information System for Rural Electrification) model and evaluated using a real-world dataset of 10,995 unelectrified households in rural Yoro, Honduras. The evaluation involved comparisons with established clustering algorithms, focusing on metrics such as the number of valid clusters, Levelized Cost of Electricity (LCOE), and execution time. The results demonstrate the algorithm's effectiveness in scenarios with equal and varying demands, highlighting its robustness, flexibility, and ability to achieve cost savings within shorter timeframes. Additionally, this approach enables the assessment of distribution infrastructures, such as microgrids and grid extensions, ensuring an effective power generation and distribution. The integration of the RElect_MGEC algorithm into IntiGIS results in an enhanced model that enables a comprehensive and informed decision-making process for rural electrification planning.

关键词： Constrained clustering Density-based clustering graph-based clustering Rural electrification Geospatial analysis Techno-economic software tool

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Spatial-Spectral clustering With Anchor graph for Hyperspectral Image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Wang, Qi Miao, Yanling Chen, Mulin Yuan, Yuan Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters without labeled training data, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, have shown dominant performance. However, the high dimensionality of HSI data makes it hard to construct the pairwise adjacent graph. Besides, abundant spatial structures are often overlooked during the clustering procedure. To better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The SSCAG has the following contributions: 1) the multiscale filtering module is utilized to smooth the homogeneous regions, so that it can increase the similarity and consistency of neighboring pixels and capture the multiple views of a local region with different scales;2) a new similarity metric is proposed to embed the spatial-spectral features into the combined adjacent graph, which can mine the intrinsic property structure of HSI data;and 3) the anchor graph (AG)-based strategy is adopted to construct the adjacent graph by a neighbor assignment scheme without hyperparameters, and its optimization employs singular value decomposition (SVD) to replace eigenvalue decomposition to reduce the computational complexity. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.

关键词： Hyperspectral imaging Optimization Eigenvalues and eigenfunctions Correlation clustering methods Linear programming clustering algorithms Anchor graph (AG) graph-based clustering hyperspectral image (HSI) spatial-spectral information

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Multiple Partitions Alignment with Adaptive Similarity Learning 1

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28th International Conference on Neural Information Processing

作者： Dai, Hao Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (数字)9783030922702

ISBN: (纸本)9783030922696;9783030922702

Multi-view clustering is a hot topic which aims to utilize the consistent and complementary information among different views in unsupervised setting. Most existing methods integrate the information from different views in the data space. Thus the performance can be deteriorated considering the unavoidable noises or inconsistency in views. In this paper, we propose to construct the similarity matrix for different views by utilizing adaptive similarity learning, and fuse multi-view information in partition level instead of the original data space. We further leverage an auto-weighted strategy to avoid bad performance caused by certain views. Finally, we formulate the above principles into a unified framework successfully and present the optimization algorithm to optimize the corresponding objective function. Experimental results on several commonly used datasets demonstrate the efficacy of our approach against the state-of-the-art methods.

关键词： Multi-view learning graph-based clustering Adaptive learning Data fusion Partition space

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LDFGB Algorithm for Anomaly Intrusion Detection

LDFGB Algorithm for Anomaly Intrusion Detection

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2nd IFIP TC5/8 International Conference on ICT-EurAsia

作者： Yin, Shang-nan Chen, Zhi-guo Kim, Sung-Ryul Konkuk Univ Div Internet & Multimedia Engn Seoul South Korea

ISBN: (纸本)9783642550324;9783642550317

With the development of internet technology, more and more risks are appearing on the internet and the internet security has become an important issue. Intrusion detection technology is an important part of internet security. In intrusion detection, it is important to have a fast and effective method to find out known and unknown attacks. In this paper, we present a graph-based intrusion detection algorithm by outlier detection method which is based on local deviation factor (LDFGB). This algorithm has better detection rates than a previous clustering algorithm. Moreover, it is able to detect any shape of cluster and still keep high detection rate for detecting unknown or known attacks. LDFGB algorithm uses graph-based cluster algorithm (GB) to get an initial partition of dataset which depends on a parameter of cluster precision, then we use the outlier detection algorithm to further processing the results of graph-based cluster algorithm. This measure is effective to improve the detection rates and false positive rates.

关键词： graph-based clustering outlier detection Intrusion Detection

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OIL DEPOTS DETECTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES based ON SALIENT REGION EXTRACTION 5

OIL DEPOTS DETECTION FROM HIGH RESOLUTION REMOTE SENSING IMA...

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5th International Conference on Audio, Language and Image Processing (ICALIP)

作者： Li, Chaoyang Huo, Hong Fang, Tao Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781509006540

The traditional methods of detecting oil depots usually use Hough transform and template matching, which often have lower detection rates and are difficult to implement. An efficient two-step detection framework is proposed in this paper to detect oil depots in high resolution remote sensing images. In the first stage, LC saliency model is used to detect the salient regions and shows a good performance on highlighting oil depots. In the second stage, task related targets from these salient regions are extracted by removing the irrelevant salient areas according to the special properties of the targets. According to the final shape, the area and distribution of oil depots, using image threshold segmentation and the graph-based clustering procedure, oil depots are detected with fairly good accuracy and efficiency.

关键词： Saliency detection image threshold segmentation graph-based clustering oil depots

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