scholarly journals Provision of an integrated data analysis platform for computational neuroscience experiments

2014 ◽  
Vol 16 (3) ◽  
pp. 150-169 ◽  
Author(s):  
Kamran Munir ◽  
Saad Liaquat Kiani ◽  
Khawar Hasham ◽  
Richard McClatchey ◽  
Andrew Branson ◽  
...  
Keyword(s):  
Data Analysis ◽  
Computational Neuroscience ◽  
Image Data ◽  
Building Blocks ◽  
Study Data ◽  
Scientific Workflow ◽  
Integrated Analysis ◽  
Data Sets ◽  
Biomedical Data ◽  
Content Type

Purpose – The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach – The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings – The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value – The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

The “power few” of missing persons’ cases

2020 ◽  
Vol 43 (2) ◽  
pp. 360-374
Author(s):  
Laura Huey ◽  
Lorna Ferguson ◽  
Larissa Kowalski
Keyword(s):  
Study Data ◽  
Record Management ◽  
Data Sets ◽  
Male Youth ◽  
Content Type ◽  
Missing Persons ◽  
Behavior Setting ◽  
Study Results ◽  
Repeat Rate ◽  
The City

PurposeThe purpose of this paper is to test the “power few” concept in relation to missing persons and the locations from which they are reported missing.Design/methodology/approachData on missing persons’ cases (n = 26,835) were extracted from the record management system of a municipal Canadian police service and used to create data sets of all of the reports associated with select repeat missing adults (n = 1943) and repeat missing youth (n = 6,576). From these sources, the five locations from which repeat missing adults and youth were most commonly reported missing were identified (“power few” locations). The overall frequency of reports generated by these locations was then assessed by examining all reports of both missing and repeat missing cases, and demographic and incident factors were also examined.FindingsThis study uncovers ten addresses (five for adults; five for youths) in the City from which this data was derived that account for 45 percent of all adults and 52 percent of all youth missing person reports. Even more striking, the study data suggest that targeting these top five locations for adults and youths could reduce the volume of repeat missing cases by 71 percent for adults and 68.6 percent for youths. In relation to the demographic characteristics of the study’s sample of adults and youths who repeatedly go missing, the authors find that female youth are two-thirds more likely to go missing than male youth. Additionally, the authors find that Aboriginal adults and youths are disproportionately represented among the repeat missing. Concerning the incident factors related to going missing repeatedly, the authors find that the repeat rate for going missing is 63.2 percent and that both adults and youths go missing 3–10 times on average.Practical implicationsThe study results suggest that, just as crime concentrates in particular spaces among specific offenders, repeat missing cases also concentrate in particular spaces and among particular people. In thinking about repeat missing persons, the present research offers support for viewing these concerns as a behavior setting issue – that is, as a combination of demographic factors of individuals, as well as factors associated with particular types of places. Targeting “power few” locations for prevention efforts, as well as those most at risk within these spaces, may yield positive results.Originality/valueVery little research has been conducted on missing persons and, more specifically, on how to more effectively target police initiatives to reduce case volumes. Further, this is the first paper to successfully apply the concept of the “power few” to missing persons’ cases.

Biomedical Data Mining

2009 ◽  
Vol 48 (03) ◽  
pp. 225-228 ◽  
Author(s):  
C. Combi ◽  
A. Tucker ◽  
N. Peek
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Image Data ◽  
Special Topic ◽  
Biomedical Data ◽  
Intelligent Data Analysis ◽  
Temporal Abstraction ◽  
Computationally Efficient ◽  
Search Spaces ◽  
Clinical Databases

Summary Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data mining. Characterizing current developments and challenges for researchers in the field. Reporting on current and future activities of IMIA’s working group on Intelligent Data Analysis and Data Mining. Describing the content of the selected papers in this special topic. Results and Conclusions: In the biomedical field, data mining methods are used to develop clinical diagnostic and prognostic systems, to interpret biomedical signal and image data, to discover knowledge from biological and clinical databases, and in biosurveillance and anomaly detection applications. The main challenges for the field are i) dealing with very large search spaces in a both computationally efficient and statistically valid manner, ii) incorporating and utilizing medical and biological background knowledge in the data analysis process, iii) reasoning with time-oriented data and temporal abstraction, and iv) developing end-user tools for interactive presentation, interpretation, and analysis of large datasets.

scholarly journals Self-organizing mappings on the flag manifold with applications to hyper-spectral image data analysis

2021 ◽  
Author(s):  
Xiaofeng Ma ◽  
Michael Kirby ◽  
Chris Peterson
Keyword(s):  
Data Analysis ◽  
Image Data ◽  
Building Blocks ◽  
Flag Manifold ◽  
Vector Spaces ◽  
Mathematical Framework ◽  
Basic Algorithm ◽  
Geometric Framework ◽  
Fixed Type ◽  
Self Organizing

AbstractA flag is a nested sequence of vector spaces. The type of the flag encodes the sequence of dimensions of the vector spaces making up the flag. A flag manifold is a manifold whose points parameterize all flags of a fixed type in a fixed vector space. This paper provides the mathematical framework necessary for implementing self-organizing mappings on flag manifolds. Flags arise implicitly in many data analysis contexts including wavelet, Fourier, and singular value decompositions. The proposed geometric framework in this paper enables the computation of distances between flags, the computation of geodesics between flags, and the ability to move one flag a prescribed distance in the direction of another flag. Using these operations as building blocks, we implement the SOM algorithm on a flag manifold. The basic algorithm is applied to the problem of parameterizing a set of flags of a fixed type.

scholarly journals Multiomics data analysis using tensor decomposition based unsupervised feature extraction --Comparison with DIABLO--

2019 ◽  
Author(s):  
Y-h. Taguchi
Keyword(s):  
Feature Extraction ◽  
Data Analysis ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Data Sets ◽  
Data Set ◽  
Advanced Method ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Unsupervised Feature Extraction

AbstractMultiomics data analysis is the central issue of genomics science. In spite of that, there are not well defined methods that can integrate multomics data sets, which are formatted as matrices with different sizes. In this paper, I propose the usage of tensor decomposition based unsupervised feature extraction as a data mining tool for multiomics data set. It can successfully integrate miRNA expression, mRNA expression and proteome, which were used as a demonstration example of DIABLO that is the recently proposed advanced method for the integrated analysis of multiomics data set.

A knowledge management based framework for enhancing the learning culture in architectural design firms in developing countries

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ayman Ahmed Ezzat Othman ◽  
Mirna Mohamed ElKady
Keyword(s):  
Developing Countries ◽  
Knowledge Management ◽  
Data Analysis ◽  
Construction Industry ◽  
Architectural Design ◽  
Building Blocks ◽  
Learning Culture ◽  
Continuous Learning ◽  
Content Type ◽  
Design Firms

Purpose In spite of the active role of continuous learning on improving organisational performance, the construction industry generally and architectural design firms (ADFs) in particular are criticised for their inability to use organisational knowledge to foster learning culture towards enhancing their performance. This paper aims to develop a framework based on knowledge management (KM) to enhance the learning culture in ADFs in developing countries. Design/methodology/approach To achieve the abovementioned aim, a research methodology consisted of data collection, data analysis and action required is designed to achieve four objectives. First, to examine the nature of the construction industry in developing countries, learning culture in ADFs, as well as knowledge and KM; second, to present three case studies to investigate the effectiveness of KM in enhancing the learning culture in ADFs; third, to investigate the perception and application of KM towards enhancing the learning culture in ADFs in Egypt, finally to develop a KM based framework to enhance the learning culture in ADFs in developing countries. Findings Through literature review, the research highlighted the fragmented nature of the architectural design process, which led to the loss of valuable information and made the process of capturing and sharing knowledge a hard task. In addition, it identified the barriers of implementing KM and the building blocks of learning culture in ADFs. Results of data analysis showed that “lack of organisational culture” and “low involvement of top management” were ranked the highest barriers for implementing KM in ADFs. Moreover, respondents mentioned that they do not share openly their information with other employees to maintain their uniqueness and that the strict working environment of their ADFs is not encouraging creativity or enhancing learning culture. Furthermore, “continuous learning and enhancement” and “experimentation, feedback and reflection” were ranked by respondents as the highest building blocks of a learning organisation. Research limitations/implications This research focussed on ADFs in developing countries. Practical implications Implementing KM strategies will facilitate the enhancement of learning culture within ADFs in developing countries. This will impact positively on improving the performance and increasing the competitiveness and market share of ADFS. Originality/value The research identified the barriers of KM implementation in ADFs and the building blocks of creating a learning organisations. It focusses on improving the performance of ADFs through using the capabilities of KM towards building learning culture in ADFs. The proposed framework which was designed to facilitate the implementation of KM for enhancing the learning culture in ADFs in developing countries represents a synthesis that is novel and creative in thought and adds value to the knowledge in a manner that has not previously occurred.

scholarly journals Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph Ogris ◽  
Yue Hu ◽  
Janine Arloth ◽  
Nikola S. Müller
Keyword(s):  
Data Analysis ◽  
Network Inference ◽  
Comprehensive Evaluation ◽  
Integrated Analysis ◽  
Data Sets ◽  
Full Potential ◽  
Omics Data ◽  
General Applicability ◽  
Full Spectrum ◽  
Fully Integrated

AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo (https://github.com/cellmapslab/kimono). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

scholarly journals Sharing and reusing cell image data

2018 ◽  
Vol 29 (11) ◽  
pp. 1274-1280 ◽  
Author(s):  
Assaf Zaritsky
Keyword(s):  
Data Analysis ◽  
Image Data ◽  
Hypothesis Generation ◽  
Data Sets ◽  
Scientific Publications ◽  
Data Standardization ◽  
Cell Image ◽  
Huge Data ◽  
Funding Agencies ◽  
Microscopy Data

The rapid growth in content and complexity of cell image data creates an opportunity for synergy between experimental and computational scientists. Sharing microscopy data enables computational scientists to develop algorithms and tools for data analysis, integration, and mining. These tools can be applied by experimentalists to promote hypothesis-generation and discovery. We are now at the dawn of this revolution: infrastructure is being developed for data standardization, deposition, sharing, and analysis; some journals and funding agencies mandate data deposition; data journals publish high-content microscopy data sets; quantification becomes standard in scientific publications; new analytic tools are being developed and dispatched to the community; and huge data sets are being generated by individual labs and philanthropic initiatives. In this Perspective, I reflect on sharing and reusing cell image data and the opportunities that will come along with it.

An Overview of Data Veracity Issues in Ship Performance and Navigation Monitoring

2018 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo
Keyword(s):  
Data Analysis ◽  
Anomaly Detection ◽  
Domain Knowledge ◽  
Study Data ◽  
Data Sets ◽  
Data Handling ◽  
Navigation Data ◽  
Industrial Iot ◽  
Ship Performance

An overview of data veracity issues in ship performance and navigation monitoring in relation to data sets collected from a selected vessel is presented in this study. Data veracity relates to the quality of ship performance and navigation parameters obtained by onboard IoT (internet of things). Industrial IoT can introduce various anomalies into measured ship performance and navigation parameters and that can degrade the outcome of the respective data analysis. Therefore, the identification and isolation process of such data anomalies can play an important role in the outcome of ship performance and navigation monitoring. In general, these data anomalies can be divided into sensor and data acquisition (DAQ) faults and system abnormal events. A considerable amount of domain knowledge is required to detect and classify such data anomalies, therefore data anomaly detection layers are proposed in this study for the same purpose. These data anomaly detection layers are divided into several levels: preliminary and advanced levels. The outcome of a preliminary anomaly detection layer with respect to ship performance and navigation data sets of a selected vessel is presented with the respective data handling challenges as the main contribution of this study.

Indoor and outdoor image classification

2019 ◽  
Vol 7 (4) ◽  
pp. 150-161
Author(s):  
Rajasekar Velswamy ◽  
Sorna Chandra Devadass ◽  
Karunakaran Velswamy ◽  
Jeyakrishnan Venugopal
Keyword(s):  
Object Recognition ◽  
Discrete Cosine Transform ◽  
Image Data ◽  
Image Database ◽  
Color Histogram ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Transform Coefficients ◽  
The Given

Purpose The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes. Design/methodology/approach For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. Findings This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly. Originality/value Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

scholarly journals A scalable, secure, and interoperable platform for deep data-driven health management

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Amir Bahmani ◽  
Arash Alavi ◽  
Thore Buergel ◽  
Sushil Upadhyayula ◽  
Qiwen Wang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Health Management ◽  
Wearable Sensors ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Time Data ◽  
Individual Level ◽  
The Individual

AbstractThe large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

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