<title>Remote transformation and local 3D reconstruction and visualization of biomedical data sets in Java3D</title>

To solve the problem that traditional multi-label support vector machine (SVM) classification algorithm adopting nonlinear kernel has been severely restricted from being used on large-scale data sets, we propose fast multi-label low-rank-linearized SVM classification algorithm based on approximate extreme points (AEML-LLSVM). First, it adopts the approximate extreme points' method to obtain representative sets from the training data set. Then, the approximate extreme points' low-rank-linearized SVM (AELLSVM) is trained on the representative sets. The AELLSVM integrates the advantages of approximate extreme points' method and LLSVM. Experimental results on three large-scale multi-label data sets have proven that the training and the testing speed of AEML-LLSVM classification algorithm are greatly improved under the premise that its classification performance is similar to that of ML-LIBSVM classification algorithm and superior to that of other fast multi-label SVM classification algorithms.

If the training data sets are large-scale, the SVM models turn into large-scale quadratic programming problems and are hard to be effectively solved. That is the main reason why the learning efficiency of SVM is so low on large-scale data sets. According to the principle of SVM, the border support vectors play a decisive role on the SVM decision hyper-plane. The nest template ant clustering algorithm in kernel space is proposed. The algorithm is used to extract border support vectors from large-scale training datasets. When using the less border support vectors to train SVM, the scale of SVM is reduced and the training performance is improved. The algorithm has better adaptability than the kernel K-means algorithm. Experimental results on UCI datasets show that the algorithm is effective, and the classification accuracy of SVM is still maintained.

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

Frequent Itemset Mining is one of the most investigated fields of data mining. It is expensive to mine frequent itemsets for a large scale data set. Especially when some data is added into the data set, it is still time-consuming from the scratch to re-compute the complete data set to update the frequent itemsets of the data set. Aiming to improve the performance of frequent itemset mining for large scale and dynamic data set, we propose a new incremental Apriori algorithm based on Spark. It reuses existing results from previous computation to modify the frequent itemsets according to the newly added data, which avoid massive recomputation. The newly proposed algorithm also takes full advantage of distributed resources with the support of Spark. Analysis in theory ensures that the newly proposed algorithm is correct. Experiments on the real-world data set demonstrate that the newly proposed algorithm effectively avoids reduplicated computation and improves the performance of frequent itemset mining with no additional storage overhead.

BackgroundGene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment.MethodsWe categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets.ResultsWe used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method.ConclusionsThis study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Organizing webpages into hot topics is one of the key steps to understand the trends from multi-modal web data. To handle this pressing problem, Poisson Deconvolution (PD), a state-of-the-art method, recently is proposed to rank the interestingness of web topics on a similarity graph. Nevertheless, in terms of scalability, PD optimized by expectation-maximization is not sufficiently efficient for a large-scale data set. In this paper, we develop a Stochastic Poisson Deconvolution (SPD) to deal with the large-scale web data sets. Experiments demonstrate the efficacy of the proposed approach in comparison with the state-of-the-art methods on two public data sets and one large-scale synthetic data set.

Tomato Functional Genomics Database (TFGD) provides a comprehensive resource to store, query, mine, analyze, visualize and integrate large-scale tomato functional genomics data sets. The database is functionally expanded from the previously described Tomato Expression Database by including metabolite profiles as well as large-scale tomato small RNA (sRNA) data sets. Computational pipelines have been developed to process microarray, metabolite and sRNA data sets archived in the database, respectively, and TFGD provides downloads of all the analyzed results. TFGD is also designed to enable users to easily retrieve biologically important information through a set of efficient query interfaces and analysis tools, including improved array probe annotations as well as tools to identify co-expressed genes, significantly affected biological processes and biochemical pathways from gene expression data sets and miRNA targets, and to integrate transcript and metabolite profiles, and sRNA and mRNA sequences. The suite of tools and interfaces in TFGD allow intelligent data mining of recently released and continually expanding large-scale tomato functional genomics data sets. TFGD is available at http://ted.bti.cornell.edu.

The conduct of investigational studies that involve large-scale data sets presents significant challenges related to the discovery and testing of novel hypotheses capable of supporting in silico discovery science. The use of what are known as Conceptual Knowledge Discovery in Databases (CKDD) methods provides a potential means of scaling hypothesis discovery and testing approaches for large data sets. Such methods enable the high-throughput generation and evaluation of knowledge-anchored relationships between complexes of variables found in targeted data sets. The authors have conducted a multipart model formulation and validation process, focusing on the development of a methodological and technical approach to using CKDD to support hypothesis discovery for in silico science. The model the authors have developed is known as the Translational Ontology-anchored Knowledge Discovery Engine (TOKEn). This model utilizes a specific CKDD approach known as Constructive Induction to identify and prioritize potential hypotheses related to the meaningful semantic relationships between variables found in large-scale and heterogeneous biomedical data sets. The authors have verified and validated TOKEn in the context of a translational research data repository maintained by the NCI-funded Chronic Lymphocytic Leukemia Research Consortium. Such studies have shown that TOKEn is: (1) computationally tractable; and (2) able to generate valid and potentially useful hypotheses concerning relationships between phenotypic and biomolecular variables in that data collection. The TOKEn model represents a potentially useful and systematic approach to knowledge synthesis for in silico discovery science in the context of large-scale and multidimensional research data sets.

The appropriateness of using statistical data to inform the design of any given service development or initiative often depends upon judgements regarding scale. Large-scale data sets, perhaps national in scope, whilst potentially important in informing the design, implementation and roll-out of experimental initiatives, will often remain unused for small-scale interventions involving only small numbers of service users or participants. This can be the result of legitimate concerns regarding representativity and rigour. Whilst large data sets may allow for strong claims to made regarding trends and tendencies with the population, a small scale intervention involving perhaps only tens of participants will not offer the opportunity to devise developmental rationales that are supported by such statistical rigour. This creates a situation in which important pilot studies and ‘suck-it-and-see’ interventions are conducted in a basic empirical fashion that does not draw upon the informed reflection that large scale data makes possible. This article will argue that such a situation is not necessary. Through graduated and selective engagement with large-scale data sets it is possible to make good use of what they offer to inform the design and up-scaling of small pilot interventions. The discussion will focus on the case of a small intervention involving just nine parents and their babies conducted in Liverpool schools. More specifically it will consider the ways in which one national data-set, the UK Millennium Cohort Study (MCS), can be used to develop this project. It will do this by way of profiling the MCS in order to both select out those aspects that could or should not be used and those aspects that could be used beneficially to inform the development of the initiative. In the process it will devise a set of criteria that could usefully be adapted for use by others in relation to service situations in which similar issues of scalar mis-match between available data-sets and service design exist.

Drawing on existing theoretical work, as well as field research, this dissertation examines the impact of medical imaging technologies on communication between physicians and older patients when diagnostics often privilege disembodied data over the patient voice. Current diagnostic trends are contextualized within the history of medicine, from Ancient Greece to the present, including the development of imaging. Since the 1970s, advanced medical imaging technologies (e.g., ultrasound, computed tomography, magnetic resonance imaging) have become the diagnostic norm in Western medicine. The rapidity of this shift, which renders the human body as flattened data, can outstrip considerations of the implications of applying such technologies to living patients. Focusing on older patients, who may be less technologically savvy than younger patients or medical professionals, the field research begins with semi-structured interviews of patients over age sixty-five, exploring their encounters with medical imaging equipment and professionals. This data is interrogated qualitatively using Foucauldian discourse analysis drawing on Andrea Doucet’s model of slow scholarship, and informed by Arthur Frank’s notion of letting stories breathe; themes were allowed to surface from the patients’ narratives, rather than imposed by the researcher. Information emerging from the data considers patients’ emotions, unexpected physical sensations, communicative strategies and rationalizations, as well as Foucauldian allusions to power. Observational research was also conducted during encounters between physicians and simulated patients in the presence of medical images; these encounters were followed by reflective exit interviews. Research indicates that although physicians are increasingly trained in patient-centred communication, it is not always optimally practised. Physicians are sometimes more comfortable with the medical discourse of disease than with the emotional, metaphoric language of the patient’s illness experience. Since the development of modern Western medicine in Europe of the late 1700s, physicians have been trained to seek pathology, with the increasing aid of medical technologies, rather than listening to their patients. For older patients, who may experience multiple co-morbidities, the lack of communication around advanced medical technologies can increase their sense of vulnerability and anxiety. The dissertation concludes with recommendations for both patients and practitioners to improve communication in the medical context.

Scientific advances build on reproducible researches which need publicly available benchmark data sets. The computer vision and speech recognition communities have led the way in establishing benchmark data sets. There are much less data sets available in mobile computing, especially for rich locomotion and transportation analytics. This paper presents a highly versatile and precisely annotated large-scale data set of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users. The data set comprises seven months of measurements, collected from all sensors of four smartphones carried at typical body locations, including the images of a body-worn camera, while three participants used eight different modes of transportation in the south-east of the U.K., including in London. In total, 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals. The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 h of labeled data and 17 562 km of traveled distance. We present how we set up the data collection, including the equipment used and the experimental protocol. We discuss the data set, including the data curation process, the analysis of the annotations, and of the sensor data. We discuss the challenges encountered and present the lessons learned and some of the best practices we developed to ensure high quality data collection and annotation. We discuss the potential applications which can be developed using this large-scale data set. In particular, we present how a machine-learning system can use this data set to automatically recognize modes of transportations. Many other research questions related to transportation analytics, activity recognition, radio signal propagation and mobility modeling can be addressed through this data set. The full data set is being made available to the community, and a thorough preview is already published.

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. To resolve problems of globalization, random-write and duration in Hadoop, a data indexing approach on Hadoop using the Java Persistence API (JPA) is elaborated in the implementation of a KD-tree algorithm on Hadoop. An improved intersection algorithm for distributed data indexing on Hadoop is proposed, it performs O(M+logN), and is suitable for occasions of multiple intersections. We compare the data indexing algorithm on open dataset and synthetic dataset in a modest cloud environment. The results show the algorithms are feasible in large-scale data mining.

Incomplete multi-view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi-view data in real-world applications. However, existing methods often struggle to effectively deal with large-scale datasets, particularly those with a significant number of missing instances. To address these issues, we propose a novel method called Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View Data (HENRI). HENRI utilizes the consensus hubs of all views to identify informative anchors to handle large-scale incomplete datasets. Furthermore, it incorporates a novel sample-level fusion strategy that effectively integrates information from all views, leading to remarkable outcomes in both cluster formation and missing data reconstruction. HENRI demonstrates exceptional capability in capturing the underlying structures of the data and recovering missing information, even when faced with a significant number of instances with incomplete data in partial views. To validate its effectiveness, we conducted experiments on 6 complete datasets and 31 incomplete datasets, comparing against 11 baseline methods. The results are impressive, demonstrating the superior performance of HENRI over the state-of-the-art methods.

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G E interactions observed in fields.

Comparative Analysis of Independent Ex Vivo functional Drug Screens Identifies Predictive Biomarkers of BCL-2 Inhibitor Response in AML