Large Data Repositories Research Articles

Analysis of copy number loss of the ErbB4 receptor tyrosine kinase in glioblastoma.

Current treatments for glioblastoma multiforme (GBM)—an aggressive form of brain cancer—are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individual’s tumor. Most receptor tyrosine kinases—such as EGFR—act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses prior to investing experimental resources.

The Listening and Spoken Language Data Repository: Design and Project Overview.

The purpose of the Listening and Spoken Language Data Repository (LSL-DR) was to address a critical need for a systemwide outcome data-monitoring program for the development of listening and spoken language skills in highly specialized educational programs for children with hearing loss highlighted in Goal 3b of the 2007 Joint Committee on Infant Hearing position statement supplement. The LSL-DR is a multicenter, international data repository for recording and tracking the demographics and longitudinal outcomes achieved by children who have hearing loss who are enrolled in private, specialized programs focused on supporting listening and spoken language development. Since 2010, annual speech-language-hearing outcomes have been prospectively obtained by qualified clinicians and teachers across 48 programs in 4 countries. The LSL-DR has been successfully implemented, bringing together the data collection efforts of these programs to create a large and diverse data repository of 5,748 children with hearing loss. Due to the size and diversity of the population, the range of assessments entered, and the demographic information collected, the LSL-DR will provide an unparalleled opportunity to examine the factors that influence the development of listening in spoken language in this population.

ESports: A new window on neurocognitive expertise?

Understanding the neurological changes that take place as expertise develops is a central topic in both cognitive psychology and cognitive neuroscience. Here, we argue that video games, despite previous misconceptions, are an excellent model environment from which one can examine the development of neurocognitive expertise. Of particular relevance we argue is the area of esports, which encompass video/computer games played within the medium of cyberspace competitively and increasingly professionally. The massive scale of participation, controlled environments, structured skill ratings, pervasive social nature, and large repositories of data, together make esports potentially a very fruitful area for scientific research to increase our understanding of a new era of cognitive athletes. This chapter reviews the progress and prospects for esports research with a particular focus on the effects of gaming on neurocognition. We also outline some exciting new avenues and techniques from which we hope to further elucidate the benefits of esports on the brain.

It�s All in the Notes: What Session Notes Can Tell Us About the Work of Writing Centers

Aim: This research note focuses on how corpus analysis tools can help researchers make sense of the data writing centers collect. Writing centers function, in many ways, like large data repositories; however, this data is under-analyzed. One example of data collected by writing centers is session notes, often collected after each consultation. The four institutions featured in this note—Michigan State University, the University of Michigan, Texas A&M University, and The Ohio State University—have analyzed a subset of their session notes, over 44,000 session notes comprising around 2,000,000 words. By analyzing the session notes using tools such as Voyant, a web-based application for performing text analysis, writing center researchers can begin to explore critically their large data repositories to understand and establish evidence-based practice, as well as to shape external messaging about writing center labor—separate from and in addition to impact on student writers—to institutional administrators, state legislators, and other stakeholders.Problem Foundation: This section identifies a key problem in writing centers—there are large amounts of data but no easy way to analyze that data. Session notes are a common record-keeping practice in writing centers, yet few researchers have critically examined these documents, and fewer still have utilized any type of discourse, textual, or corpus analysis to do so. Those studies that do are either limited in scope (Brown, 2010) or are labor-intensive because they require hand coding (Hall, 2017). Additionally, few cross-institutional collaborations or partnerships exist among writing center research. Therefore, our experiment with corpus-level analysis of session notes has the potential to break new ground in writing center studies that can lead to innovations in training, assessment, and field-based practices.Information Collection: This research note uses Voyant, a free open-access web-based application, to perform a textual analysis on session notes from four institutions. Institutional writing centers each analyzed 500,000 words from session notes encompassing the last two to five years. Each institution has a different audience for its session notes. OSU, MSU, and TAMU share documents with clients upon request. TAMU’s online writing center automatically sends notes to clients, while University of Michigan only shares notes among staff (not instructors or students). Voyant is one tool among many (e.g., AntConc, CohMetrix, KNIME Analytics Platform, and Word Stat) that researchers can use to identify their analytical priorities, whether it be structural linguistics, contextual linguistics, socio-cultural discourse, or some combination therein. Because the field of writing center studies is only just starting to apply corpus analysis, we suggest that as corpora are developed within the field, corpus analysis can become more sophisticated and varied, and different programs can serve different functions and needs. Programs such as Voyant are free multi-featured and open-access online programs that provide high-quality visuals. For this project, each researcher chose which tools in Voyant to use based on the interests and goals of the institution and writing center. Tools used include Corpus Terms (a table view of term frequency in the entire corpus), Cirrus (a word cloud that visualizes the top frequency words of a corpus), Contexts Tool (which shows each occurrence of a keyword and how it co-occurs with words/phrases to the left and right of it), and Collocates Graph/Links Tool (which represents keywords and terms that occur in close proximity as a force directed network graph).Conclusions: Tools such as Voyant offer an effective way to provide broad insights into the work of writing centers through a corpus analysis of session notes. As each institution demonstrates, a wide variety of questions are answerable by tools like Voyant, including ones that are specific to individual centers and institutions. Corpus analysis of session notes can provide a broad view of the ways that language functions within sessions to enhance and concretize writing centers’ sense of the work they, and their consultants, do. While corpus analysis does not provide all of the answers for writing centers, when this analytic method is coupled with other quantitative and qualitative strategies for understanding the work of writing centers and the interactions that take place therein, it can provide us with the insights needed to support consultants and clients while improving the center.Directions for Further Research: While this research note demonstrates the capability of analytical tools like Voyant to help individual institutions understand and assess their writing centers, directions for further research include comparing the institutions as well as creating a corpus of the combined institutions’ session notes. Because there are no current reference corpora for writing centers, as there are for contemporary American English (for example, the Corpus of Contemporary American English (COCA), this collaboration can aggregate and create corpora for other institutions to utilize in their own analyses. By creating a reference corpus, we can extend the research impact of our work and make findings more powerful and, potentially, significant as other writing centers take up this work.

Schema Integration of Web Tables (SIWeT)

Schema integration has been mainly applied in database environment whether schemas to be integrated belonged to a single organization or multiple ones. Schema extraction is a relatively new area where schema is extracted from a web table. The extracted schema is not as much concretely defined as in a typical database environment. The work in this paper brings two areas together where extracted schemas from multiple web tables are integrated to form a global schema. The data are also extracted from web tables and placed into global table. This creates a large repository of data of the same domain extracted dynamically from websites which is then available for different types of ad-hoc queries. This work also imposes challenges on schema integration to be studied in the context of schema extraction and other way round.

Mass Spectrometry-Based Identification of Extracellular Domains of Cell Surface N-Glycoproteins: Defining the Accessible Surfaceome for Immunophenotyping Stem Cells and Their Derivatives.

Human stem cells and their progeny are valuable for a variety of research applications and have the potential to revolutionize approaches to regenerative medicine. However, we currently have limited tools to permit live isolation of homogeneous populations of cells apt for mechanistic studies or cellular therapies. While these challenges can be overcome through the use of immunophenotyping based on accessible cell surface markers, the success of this process depends on the availability of reliable antibodies and well-characterized markers, which are lacking for most stem cell lineages. This chapter outlines an iterative process for the development of new cell surface marker barcodes for identifying and selecting stem cell derived progeny of specific cell types, subtypes, and maturation stages, where antibody-independent identification of cell surface proteins is achieved using a modern chemoproteomic approach to specifically identify N-glycoproteins localized to the cell surface. By taking advantage of a large repository of available cell surfaceome data, proteins that are unlikely to confer cell type specificity can be rapidly eliminated from consideration. Subsequently, targeted quantitation by mass spectrometry can be used to refine candidates of interest, and a bioinformatic visualization tool is key to mapping experimental data to candidate protein sequences for the purpose of epitope selection during the antibody development phase. Overall, the process of developing cell surface barcodes for immunophenotyping is iterative and can include multiple rounds of discovery, refinement, and validation depending on the phenotypic resolution required.

Big Text advantages and challenges: classification perspective

Big Text, i.e., large repositories of textual data, is a part of Big Data. In total, 80–85 % of Big Text comes in unstructured form, with significant contribution from social media. In this position paper, we discuss Big Text advantages and challenges in respect to text classification. We propose a new approach to performance evaluation of classification algorithms when they applied to Big Text, namely, using corpora comparison in the result evaluation. We also discuss a significant increase in texts with comprehensive information and challenges Big Text methods face in analysis of such texts.

Comprehensive survey on data warehousing research

Data, information and knowledge have important role in various human activities because by processing data, information is extracted and by analyzing data and information the knowledge is extracted. The problem of storing, managing and analyzing the huge volumes of data, which is generated regularly by the various sources has been arisen which leads to the need of large data repositories, e.g. data warehouses. In view of the above, a considerable amount attention of research and industry has been attracted by the data warehousing (DW). Various issues and challenges in the field of data warehousing are presented in many studies during the recent years. In this paper, a comprehensive survey is presented to take a holistic view of the research trends in the fields of data warehousing. This paper presents a systematic division of work of researchers in the fields of data warehousing. Finally, current research issues and challenges in the area of data warehousing are summarized for future directions.

SATORI: a system for ontology-guided visual exploration of biomedical data repositories

MotivationThe ever-increasing number of biomedical datasets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide context by semantically relating datasets. Visualizing this rich network of relationships can improve the explorability of large data repositories and help researchers find datasets of interest.ResultsWe developed SATORI—an integrative search and visual exploration interface for the exploration of biomedical data repositories. The design is informed by a requirements analysis through a series of semi-structured interviews. We evaluated the implementation of SATORI in a field study on a real-world data collection. SATORI enables researchers to seamlessly search, browse and semantically query data repositories via two visualizations that are highly interconnected with a powerful search interface.Availability and implementationSATORI is an open-source web application, which is freely available at http://satori.refinery-platform.org and integrated into the Refinery Platform.Supplementary information Supplementary data are available at Bioinformatics online.

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer

Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen receptors, progesterone receptors and HER2 receptors are difficult to treat. Regarding large data repositories such as The Cancer Genome Atlas, available wet-lab methods for establishing the presence of these receptors do not always conclusively cover all available samples. To this end, we introduce median-supplement methods to identify hormonal and HER2 receptor status phenotypes of breast cancer patients using gene expression profiles. In these approaches, supplementary instances based on median patient gene expression are introduced to balance a training set from which we build simple models to identify the receptor expression status of patients. In addition, for the purpose of benchmarking, we examine major machine learning approaches that are also applicable to the problem of finding receptor status in breast cancer. We show that our methods are robust and have high sensitivity with extremely low false-positive rates compared with the well-established methods. A successful application of these methods will permit the simultaneous study of large collections of samples of breast cancer patients as well as save time and cost while standardizing interpretation of outcomes of such studies.

Improving the Generation of Infoboxes from Data Silos through Machine Learning and the Use of Semantic Repositories

Nowadays, both public and private organizations own large private text-based data repositories with critical information. The information stored in these data silos is usually queried through information retrieval systems based on indexes, which yield hundreds or thousands of results when interrogated using keywords. In order to improve data accessibility when searching for specific information, the use of infoboxes can be very useful. The generation such infoboxes is by itself a complex problem, but in this type of isolated environments, it becomes even harder as the selection of the entities and their attributes can be conditioned by local and very specific parameters. In this work, we propose a methodology to tackle this special problem, combining classical approaches with machine learning, and leveraging the resources provided by the Semantic Web. The working methodology has been applied to two well-known datasets, and also it has been tested on a real environment scenario, showing the feasibility of our approach.

DPM evolution: a disk operations management engine for DPM

The DPM (Disk Pool Manager) project is the most widely deployed solution for storage of large data repositories on Grid sites, and is completing the most important upgrade in its history, with the aim of bringing important new features, performance and easier long term maintainability. Work has been done to make the so-called “legacy stack” optional, and substitute it with an advanced implementation that is based on the fastCGI and RESTful technologies. Beside the obvious gain in making optional several legacy components that are difficult to maintain, this step brings important features together with performance enhancements. Among the most important features we can cite the simplification of the configuration, the possibility of working in a totally SRM-free mode, the implementation of quotas, free/used space on directories, and the implementation of volatile pools that can pull files from external sources, which can be used to deploy data caches. Moreover, the communication with the new core, called DOME (Disk Operations Management Engine) now happens through secure HTTPS channels through an extensively documented, industry-compliant protocol. For this leap, referred to with the codename “DPM Evolution”, the help of the DPM collaboration has been very important in the beta testing phases, and here we report about the technical choices.

Improving accuracy of missing data imputation in data mining

In fact, raw data in the real world is dirty. Each large data repository contains various types of anomalous values that influence the result of the analysis, since in data mining, good models usually need good data, databases in the world are not always clean and includes noise, incomplete data, duplicate records, inconsistent data and missing values. Missing data is a common drawback in many real-world data sets. In this paper, we proposed an algorithm depending on improving (MIGEC) algorithm in the way of imputation for dealing missing values. We implement grey relational analysis (GRA) on attribute values instead of instance values, and the missing data were initially imputed by mean imputation and then estimated by our proposed algorithm (PA) used as a complete value for imputing next missing value.We compare our proposed algorithm with several other algorithms such as MMS, HDI, KNNMI, FCMOCS, CRI, CMI, NIIA and MIGEC under different missing mechanisms. Experimental results demonstrate that the proposed algorithm has less RMSE values than other algorithms under all missingness mechanisms.

Volunteered Geographic Videos in Physical Geography: Data Mining from YouTube

Volunteered geographic information and citizen science have advanced academic and public understanding of geographical and ecological processes. Videos hosted online represent a large data source that could potentially provide meaningful results for studies in physical geography—a concept we term volunteered geographic videos (VGV). Technological advances in image-capturing devices, computing, and image processing have resulted in increasingly sophisticated methods that treat imagery as raw data, such as resolving high-resolution topography with structure from motion or the calculation of surface flow velocity in rivers with particle image velocimetry. The ubiquitous nature of recording devices and citizens who share imagery online have resulted in a vast archive of potentially useful online videos. This article analyzes the potential for using YouTube videos for research in physical geography. We discuss the combination of suitability and availability that has made this possible and emphasize the distinction between moderately suitable imagery that can directly answer research questions and lower suitability imagery that can indirectly support a study. We present example case studies that address (1) initial considerations of using VGV, (2) topographic data extraction from a video taken after a landslide, and (3) data extraction from a video of a flash flood that could support a study of extreme floods and wood transport. Finally, we discuss both the benefits and complicating factors associated with VGV. The results indicate that VGV could be used to support certain studies in physical geography and that this large repository of raw data has been underutilized.

Detecting variation of emotions in online activities

Abstract Online text sources form evolving large scale data repositories out of which valuable knowledge about human emotions can be derived. Beyond the primary emotions which refer to the global emotional signals, deeper understanding of a wider spectrum of emotions is important to detect online public views and attitudes. The present work is motivated by the need to test and provide a system that categorizes emotion in online activities. Such a system can be beneficial for online services, companies recommendations, and social support communities. The main contributions of this work are to: (a) detect primary emotions, social ones, and those that characterize general affective states from online text sources, (b) compare and validate different emotional analysis processes to highlight those that are most efficient, and (c) provide a proof of concept case study to monitor and validate online activity, both explicitly and implicitly. The proposed approaches are tested on three datasets collected from different sources, i.e., news agencies, Twitter, and Facebook, and on different languages, i.e., English and Greek. Study results demonstrate that the methodologies at hand succeed to detect a wider spectrum of emotions out of text sources.

Particle Swarm Optimization based incremental classifier design for rice disease prediction

Increase of huge amount of data in every application demands an incremental learning technique for data analysis. One of such data analysis task in dynamic environment is to design an incremental classifier for decision making and consequently updating the knowledge base of the overall system. Classifier construction depicts extraction of interesting patterns from the large repository of data and predicts the future trends based on the existing patterns. The time complexity of the classification system increases gradually and the system becomes inefficient while it is learned repeatedly for adding new group of data with the existing one in a certain interval of time. Without learning the same classifier for the whole data, if the knowledge of old data extracted by the classifier is used together with the new group of data to design the updated classifier, called incremental classifier, then time complexity reduces drastically. In the paper, the concepts of Particle Swarm Optimization technique and Association Rule Mining are used to design an incremental rule based classification system. The incremental classifier is suitable to apply on rice disease dataset for disease prediction as the characteristics of rice diseases change in time due to change of climate, biological, and geographical factors. The proposed method has been applied on both simulated rice disease dataset and benchmark datasets and the classification accuracy is measured and compared with various state of the art classification algorithms. The method is also evaluated based on some statistical measures and statistical test is done to establish its significance and effectiveness.

Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approach

Residences for geriatric patients are usually understaffed, with each caregiver being in charge of several residents. A caregiver must assess the well being of residents and report to the medical staff if there is something unusual with a resident. Deviations from the routine will trigger an alarm, and automatic tools can help in making timely decisions.In this paper, we explore three visualization metaphors aiming at providing caregivers with an individualized overview of the activities carried out by residents in a given time frame. We postulate that this overview is sufficient to distinguish between normal and abnormal periods of time when visually compared in groups. We also present two automated approaches, data driven and knowledge driven respectively, to detect abnormalities. The visualization and the automated approaches are tested on a naturalistic dataset obtained from a long-term personalized sensing and annotation campaign in a residence for geriatric patients. Data is of two types, obtained from IoT infrastructure and wearables and from manual annotations made by the staff. Both approaches were empirically evaluated and validated in the paper. A side product of this research is a large repository of cleansed data from the sensing and annotation campaign for 45 older adults over a period of 39 months.

Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.

Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media. Our deep neural language model utilizes word embedding as the representation of text input and recognizes named entity types with the state-of-the-art Bidirectional Long Short-Term Memory (Bi-LSTM) Recurrent Neural Network. Our Bi-LSTM model achieved the best performance compared to 3 baseline models, with a precision of 94.10%, a recall of 91.80%, and an F-measure of 92.94%. We identified 1591 unique adverse events and 9930 unique e-cigarette components (ie, chemicals, flavors, and devices) from our research testbed. Although the conditional random field baseline model had slightly better precision than our approach, our Bi-LSTM model achieved much higher recall, resulting in the best F-measure. Our method can be generalized to extract medical concepts from social media for other medical applications.

Errors in Diagnosis of Spinal Epidural Abscesses in the Era of Electronic Health Records

PurposeWith this study, we set out to identify missed opportunities in diagnosis of spinal epidural abscesses to outline areas for process improvement. MethodsUsing a large national clinical data repository, we identified all patients with a new diagnosis of spinal epidural abscess in the Department of Veterans Affairs (VA) during 2013. Two physicians independently conducted retrospective chart reviews on 250 randomly selected patients and evaluated their records for red flags (eg, unexplained weight loss, neurological deficits, and fever) 90 days prior to diagnosis. Diagnostic errors were defined as missed opportunities to evaluate red flags in a timely or appropriate manner. Reviewers gathered information about process breakdowns related to patient factors, the patient–provider encounter, test performance and interpretation, test follow-up and tracking, and the referral process. Reviewers also determined harm and time lag between red flags and definitive diagnoses. ResultsOf 250 patients, 119 had a new diagnosis of spinal epidural abscess, 66 (55.5%) of which experienced diagnostic error. Median time to diagnosis in error cases was 12 days, compared with 4 days in cases without error (P <.01). Red flags that were frequently not evaluated in error cases included unexplained fever (n = 57; 86.4%), focal neurological deficits with progressive or disabling symptoms (n = 54; 81.8%), and active infection (n = 54; 81.8%). Most errors involved breakdowns during the patient–provider encounter (n = 60; 90.1%), including failures in information gathering/integration, and were associated with temporary harm (n = 43; 65.2%). ConclusionDespite wide availability of clinical data, errors in diagnosis of spinal epidural abscesses are common and involve inadequate history, physical examination, and test ordering. Solutions should include renewed attention to basic clinical skills.

Big Data Analyses in Health and Opportunities for Research in Radiology.

This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research. This article aims to invoke novel research ideas through a combination of examples of analyses and domain knowledge.