Journal articles: 'Data interaction analysis'

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Volle, Michel. "Interaction between data analysis and telecommunications." Applied Stochastic Models and Data Analysis 8, no. 1 (March 1992): 57–65. http://dx.doi.org/10.1002/asm.3150080108.

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Dias, Tiago Gerheim Souza, and Adam Bezuijen. "Data Analysis of Pile Tunnel Interaction." Journal of Geotechnical and Geoenvironmental Engineering 141, no. 12 (December 2015): 04015051. http://dx.doi.org/10.1061/(asce)gt.1943-5606.0001350.

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Fisher, Carolanne, and Penelope Sanderson. "Exploratory sequential data analysis." Interactions 3, no. 2 (March 1996): 25–34. http://dx.doi.org/10.1145/227181.227185.

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Richmond, Sally, Orli Schwartz, Katherine A. Johnson, Marc L. Seal, Katherine Bray, Camille Deane, Lisa B. Sheeber, Nicholas B. Allen, and Sarah Whittle. "Exploratory Factor Analysis of Observational Parent–Child Interaction Data." Assessment 27, no. 8 (September 15, 2018): 1758–76. http://dx.doi.org/10.1177/1073191118796557.

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The majority of studies using observational coding systems for family interaction data derive scales describing family members’ behaviors based on rational/theoretical approaches. This study explored an empirical approach to identifying the component structure of parent–child observational data that incorporated the affective context of the interaction. Dyads of 155 typically developing 8-year-olds and their mothers completed questionnaires and two interaction tasks, one each designed to illicit positive and negative interactions. Behaviors were coded based on a modified version of the Family Interaction Macro-coding System. Multiple factor analysis identified four-component solutions for the maternal and child data. For both, two of the components included negative behaviors, one positive behavior, and one communicative behavior. Evidence for the validity of the maternal and child components was demonstrated by associations with child depression and anxiety symptoms and behavioral problems. Preliminary evidence supports an empirical approach to identify context-specific components in parent–child observational data.

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Han, Ying, Liang Cheng, and Weiju Sun. "Analysis of Protein-Protein Interaction Networks through Computational Approaches." Protein & Peptide Letters 27, no. 4 (March 17, 2020): 265–78. http://dx.doi.org/10.2174/0929866526666191105142034.

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The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

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Arnau, V., S. Mars, and I. Mar n. "Iterative Cluster Analysis of Protein Interaction Data." Bioinformatics 21, no. 3 (September 16, 2004): 364–78. http://dx.doi.org/10.1093/bioinformatics/bti021.

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K., Priya. "Stock Market Interaction with US Dollar Exchange Rates Using Panel Data Analysis: Evidence from BRIC Countries." Journal of Advanced Research in Dynamical and Control Systems 12, no. 7 (July 20, 2020): 255–64. http://dx.doi.org/10.5373/jardcs/v12i7/20202007.

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Jofre, Ana, Steve Szigeti, and Sara Diamond. "Materializing data." DAT Journal 1, no. 2 (December 27, 2016): 2–14. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p2-14.

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The visualization of data elucidates trends and patterns in the phenomena that the data represents, and opens accessibility to understanding complicated human and natural processes represented by data sets. Research indicates that interacting with a visualization amplfies cognition and analysis. A single visualization may show only one facet of the data. To examine the data from multiple perspectives, engaged citizens need to be able to construct their own visualizations from a data set. Many tools for data visualization have responded to this need, allowing non-data experts to manipulate and gain insights into their data, but most of these tools are restricted to the computer screen, keyboard, and mouse. Cognition and analysis may be strengthened even more through embodied interaction with data, whether through data sculpture or haptic and tangible interfaces. We present here the rationale for the design of a tool that allows users to probe a data set, through interactions with graspable (tangible) three-dimensional objects, rather than through a keyboard and mouse interaction. We argue that the use of tangibles facilitates understanding abstract concepts, and facilitates many concrete learning scenarios. Another advantage of using tangibles over screen-based tools is that they foster collaboration, which can promote a productive working and learning environment.

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Emamjomeh, Abbasali, Darush Choobineh, Behzad Hajieghrari, Nafiseh MahdiNezhad, and Amir Khodavirdipour. "DNA–protein interaction: identification, prediction and data analysis." Molecular Biology Reports 46, no. 3 (March 26, 2019): 3571–96. http://dx.doi.org/10.1007/s11033-019-04763-1.

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Maher, Carmel, Mark Hadfield, Maggie Hutchings, and Adam de Eyto. "Ensuring Rigor in Qualitative Data Analysis." International Journal of Qualitative Methods 17, no. 1 (July 10, 2018): 160940691878636. http://dx.doi.org/10.1177/1609406918786362.

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Deep and insightful interactions with the data are a prerequisite for qualitative data interpretation, in particular, in the generation of grounded theory. The researcher must also employ imaginative insight as they attempt to make sense of the data and generate understanding and theory. Design research is also dependent upon the researchers’ creative interpretation of the data. To support the research process, designers surround themselves with data, both as a source of empirical information and inspiration to trigger imaginative insights. Constant interaction with the data is integral to design research methodology. This article explores a design researchers approach to qualitative data analysis, in particular, the use of traditional tools such as colored pens, paper, and sticky notes with the CAQDAS software, NVivo for analysis, and the associated implications for rigor. A design researchers’ approach which is grounded in a practice which maximizes researcher data interaction in a variety of learning modalities ensures the analysis process is rigorous and productive. Reflection on the authors’ research analysis process, combined with consultation with the literature, would suggest digital analysis software packages such as NVivo do not fully scaffold the analysis process. They do, however, provide excellent data management and retrieval facilities that support analysis and write-up. This research finds that coding using traditional tools such as colored pens, paper, and sticky notes supporting data analysis combined with digital software packages such as NVivo supporting data management offer a valid and tested analysis method for grounded theory generation. Insights developed from exploring a design researchers approach may benefit researchers from other disciplines engaged in qualitative analysis.

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Klise, Katherine, Walt Beyeler, Patrick Finley, and Monear Makvandi. "Analysis of mobility data to build contact networks for COVID-19." PLOS ONE 16, no. 4 (April 15, 2021): e0249726. http://dx.doi.org/10.1371/journal.pone.0249726.

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As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.

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van der Schoot, Hans, and Hasan Ural. "Data Flow Analysis of System Specifications in Lotos." International Journal of Software Engineering and Knowledge Engineering 07, no. 01 (March 1997): 43–68. http://dx.doi.org/10.1142/s0218194097000035.

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In LOTOS, a system is specified as a behaviour expression describing the externally observable behaviour of the system in terms of possible sequences of interactions between the system and its environment. The desired control flow and data flow that must be established by a possible implementation of the system are specified in the behaviour expression as implicit enumerations of allowed sequences of interaction identifiers and relationships among interaction parameters, respectively. A model exposing the desired flow of data within the allowed control flow expressed in a system specification in LOTOS is presented. Based on the explicit information provided by the model, data flow anomaly detection and data flow oriented test selection are facilitated. A comprehensive example, i.e. an alternating bit protocol specification, is used to illustrate both these validation activities. An error in this specification is revealed by the analysis of a data flow anomaly detected within the specification. A set of test paths is derived from the specification by the application of an existing data flow oriented test selection criterion, called all-uses criterion.

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Saito, Hidetoshi. "DEPENDENCE AND INTERACTION IN FREQUENCY DATA ANALYSIS IN SLA RESEARCH." Studies in Second Language Acquisition 21, no. 3 (September 1999): 453–75. http://dx.doi.org/10.1017/s0272263199003046.

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In SLA research, dependence in frequency data is a prevalent problem (Hatch & Lazaraton, 1991). Researchers usually regard the data as being independent and subject them to statistical analyses. Another problem in frequency data analysis in SLA research is the exclusion of interaction terms. This is the case because of the use of chi-square analysis or the unpopularity of multifactorial frequency data analyses. This study investigates the violation of the “independent observation” assumption as well as the effect of including interaction terms in frequency data analysis. Reanalyzing a couple of published data sets, the paper argues in favor of using multifactorial frequency data analyses over multiple chi-squares in order to take into account dependence and interaction of the frequency data. A set of recommendations for SLA researchers will be provided when statistically analyzing frequency data in SLA research.

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Larsen, Peter E., Frank Collart, and Yang Dai. "Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data." International Journal of Knowledge Discovery in Bioinformatics 1, no. 3 (July 2010): 1–19. http://dx.doi.org/10.4018/jkdb.2010070101.

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The reconstruction of protein-protein interaction (PPI) networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These biological networks have specific topologies defined by the functional and evolutionary relationships between the proteins and the physical limitations imposed on proteins interacting in the three-dimensional space. In this paper, the authors propose a novel approach for the identification of potential protein-protein interactions based on the integration of known PPI network topology and transcriptomic data. The proposed method, Function Restricted Value Neighborhood (FRV-N), was used to reconstruct PPI networks using an experimental data set consisting of 170 yeast microarray profiles. The results of this analysis demonstrate that incorporating knowledge of interactome topology improves the ability of transcriptome analysis to reconstruct interaction networks with a high degree of biological relevance.

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Shafto, Michael G., Asaf Degani, and Alex Kirlik. "Canonical Correlation Analysis of Data on Human-Automation Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 1 (October 1997): 62–65. http://dx.doi.org/10.1177/107118139704100116.

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Canonical correlation analysis is a type of multivariate linear statistical analysis, first described by Hotelling (1935), which is used in a wide range of disciplines to analyze the relationships between multiple independent and multiple dependent variables. We argue that canonical correlation analysis is the method of choice for use with many kinds of datasets encountered in human factors research, including field-study data, part-task and full-mission simulation data, and flight-recorder data. Although canonical correlation analysis is documented in standard textbooks and is available in many statistical computing packages, there are some technical and interpretive problems which prevent its routine use by human factors practitioners. These include problems of computation, interpretation, statistical significance, and treatment of discrete variables. In this paper we discuss these problems and suggest solutions to them. We illustrate the problems and their solutions based on our experience in using canonical correlation in the analysis of a field study of crew-automation interaction in commercial aviation.

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Tójar, Juan-Carlos. "Classroom Interaction Evaluation Through Sequential Analysis of Observational Data." European Journal of Psychological Assessment 12, no. 2 (May 1996): 132–40. http://dx.doi.org/10.1027/1015-5759.12.2.132.

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In this paper, an example of research in which interaction within the classroom is evaluated using a technique of sequential analysis starting from log-linear and logit models is presented. The research was carried out among students in their last year of secondary school (17-18 year olds). The general aim was to improve the teaching-learning environment through evaluating interaction. The context of study, the categorization process, the records and the observational sampling carried out and the records obtained and briefly described, are sequentially analyzed using log-linear and especially logit models, and by placing special emphasis on the concordance of the observational measurement . From the chosen model, a set of aspects of great interest is studied in depth to examine the interpretation of the results (mainly the adjusted residuals and the -ers estimated from the model). Finally, there is a discussion about a series of considerations relating to the choice of the logit models and to the usefulness and applicability of the sequential analysis in the evaluation of interaction in educational contexts.

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Ouyang, Z., R. P. Mowers, A. Jensen, S. Wang, and S. Zheng. "Custer Analysis for Genotype × Environment Interaction with Unbalanced Data." Crop Science 35, no. 5 (September 1995): 1300–1305. http://dx.doi.org/10.2135/cropsci1995.0011183x003500050008x.

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Bean, Gordon J., and Trey Ideker. "Differential analysis of high-throughput quantitative genetic interaction data." Genome Biology 13, no. 12 (2012): R123. http://dx.doi.org/10.1186/gb-2012-13-12-r123.

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Wu, Xintao, Yong Ye, and Liying Zhang. "Graphical modeling based gene interaction analysis for microarray data." ACM SIGKDD Explorations Newsletter 5, no. 2 (December 2003): 91–100. http://dx.doi.org/10.1145/980972.980984.

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Lin, C. Y., and C. S. Lin. "Investigation of genotype-environment interaction by cluster analysis in animal experiments." Canadian Journal of Animal Science 74, no. 4 (December 1, 1994): 607–12. http://dx.doi.org/10.4141/cjas94-089.

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The conventional ANOVA (F ratio of GE interaction mean squares to error mean square) provides a means to test if GE interaction is significant, but it does not tell us which factor levels are significantly different or how they are interacting. To answer the latter question, plant researchers developed a technique to group genotypes for similarity of GE interactions and through the resulting groups to explore the GE interaction structure. The basic idea of the technique is to stratify genotypes (or environments) into subgroups such that GE interactions among genotypes (or environments) are homogeneous within groups but heterogeneous among groups. This technique is introduced in this paper using an animal experiment as an example for illustration. The possibilities and limitations of applying this technique to animal data are also discussed. Key words: Genotype-environment interaction, cluster analysis

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Klinger, Allen, and Warren K. Fox. "Point data analysis." Computers & Graphics 12, no. 3-4 (January 1988): 557–64. http://dx.doi.org/10.1016/0097-8493(88)90078-7.

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HAYASHI, YOSHIHARU, MIME KOBAYASHI, KATSUYOSHI SAKAGUCHI, NAO IWATA, MASAKI KOBAYASHI, YO KIKUCHI, and YOSHIMASA TAKAHASHI. "PROTEIN CLASSIFICATION USING COMPARATIVE MOLECULAR INTERACTION PROFILE ANALYSIS SYSTEM." Journal of Bioinformatics and Computational Biology 02, no. 03 (September 2004): 497–510. http://dx.doi.org/10.1142/s0219720004000703.

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We recently introduced a new molecular description factor, interaction profile Factor (IPF) that is useful for evaluating molecular interactions. IPF is a data set of interaction energies calculated by the Comparative Molecular Interaction Profile Analysis system (CoMIPA). CoMIPA utilizes AutoDock 3.0 docking program, and the system has shown to be a powerful tool in clustering the interacting properties between small molecules and proteins. In this report, we describe the application of CoMIPA for protein clustering. A sample set of 15 proteins that share less than 20% homology and have no common functional motifs in primary structure were chosen. Using CoMIPA, we were able to cluster proteins that bound to the same small molecule. Other structural homology-based clustering programs such as PSI-BLAST or PFAM were unable to achieve the same classification. The results are striking because it is difficult to find any common features in the active sites of these proteins that share the same ligand. CoMIPA adds new dimensions for protein classification and has the potential to be a helpful tool in predicting and analyzing molecular interactions.

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Padmaja, B., V. V. Rama Prasad, and K. V. N. Sunitha. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 446. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp446-452.

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Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.

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Langer, Tristan, and Tobias Meisen. "System Design to Utilize Domain Expertise for Visual Exploratory Data Analysis." Information 12, no. 4 (March 24, 2021): 140. http://dx.doi.org/10.3390/info12040140.

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Exploratory data analysis (EDA) is an iterative process where data scientists interact with data to extract information about their quality and shape as well as derive knowledge and new insights into the related domain of the dataset. However, data scientists are rarely experienced domain experts who have tangible knowledge about a domain. Integrating domain knowledge into the analytic process is a complex challenge that usually requires constant communication between data scientists and domain experts. For this reason, it is desirable to reuse the domain insights from exploratory analyses in similar use cases. With this objective in mind, we present a conceptual system design on how to extract domain expertise while performing EDA and utilize it to guide other data scientists in similar use cases. Our system design introduces two concepts, interaction storage and analysis context storage, to record user interaction and interesting data points during an exploratory analysis. For new use cases, it identifies historical interactions from similar use cases and facilitates the recorded data to construct candidate interaction sequences and predict their potential insight—i.e., the insight generated from performing the sequence. Based on these predictions, the system recommends the sequences with the highest predicted insight to data scientist. We implement a prototype to test the general feasibility of our system design and enable further research in this area. Within the prototype, we present an exemplary use case that demonstrates the usefulness of recommended interactions. Finally, we give a critical reflection of our first prototype and discuss research opportunities resulting from our system design.

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Brito, Paula. "Symbolic Data Analysis: another look at the interaction of Data Mining and Statistics." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 4 (July 2014): 281–95. http://dx.doi.org/10.1002/widm.1133.

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Rosel, Jesús, and Ian Plewis. "Longitudinal Data Analysis with Structural Equations." Methodology 4, no. 1 (January 2008): 37–50. http://dx.doi.org/10.1027/1614-2241.4.1.37.

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Abstract. In this paper we review different structural equation models for the analysis of longitudinal data: (a) univariate models of observable variables, (b) multivariate models of observable variables, (c) models with latent variables, (d) models that are unconditioned or conditioned to other variables (depending on the variability of the independent variables: time-varying or time-invariant, and depending on the type of independent variables: of latent variables or of observable variables), (e) models with interaction of variables, (f) models with nonlinear variables, (g) models with a constant, (h) with single level and multilevel measurement, and (i) other advances in SEM of longitudinal data (latent growth curve model, latent difference score, etc.). We pay more attention to the interaction of variables and to nonlinear transformations of variables because they are not frequently used in empirical investigation. They do, however, offer interesting possibilities to researchers who wish to verify relations between the variables they obtain. Potential applications are described, with their advantages and disadvantages.

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Heath, A. C. "The Analysis of Marital Interaction in Cross-Sectional Twin Data." Acta geneticae medicae et gemellologiae: twin research 36, no. 1 (January 1987): 41–49. http://dx.doi.org/10.1017/s0001566000004578.

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AbstractThe effects on twin data of social interaction between spouses is examined. When social interaction leads to an increase in marital resemblance (eg through reciprocal imitation), the variance of married individuals is increased, compared to the variance of unmarried individuals. Furthermore, the expected correlations between concordant married twin pairs will be lower than the expected correlations between concordant unmarried twin pairs, with the discordant twin correlations being intermediate in value. It is therefore possible, in principle, to detect the effects of marital interaction without using either longitudinal data or data on spouse pairs. However, to be detectable in twin data, marital interaction must be strong, or must exhibit marked asymmetry of effects between males and females. Genotype × environment interaction can also produce heterogeneity of correlation between concordant married, discordant, and concordant unmarried twin pairs, when genetic and environmental effects interact with marital status. However, this will usually produce increased estimates of the genetic component of variance in unmarried twins, whereas marital interaction produces increased genetic variance in married twins.

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Padmaja, B., V. V. Rama Prasad, and K. V. N. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 1 (October 1, 2016): 148. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp148-154.

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<p>Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.<em></em></p>

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Sun, Zheng, Shihao Li, Fuhua Li, and Jianhai Xiang. "Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/416543.

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WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1) and the constructed transcriptome data ofF. chinensiswere used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP) encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA), two integrin beta (ITGB), and one syndecan (SDC). Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

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Pérez-Marín, Diana, and Silvia Tamayo-Moreno. "Designing Pedagogic Conversational Agents through Data Analysis." TecnoLógicas 23, no. 47 (January 30, 2020): 243–56. http://dx.doi.org/10.22430/22565337.1455.

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Pedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowledge domains. In our previous work, a methodology for designing agents was published, the result of which was Agent Dr. Roland, the first conversational agent for Early Childhood Education. In this paper, we propose the use of Data Analytics techniques to improve the design of the agent. Two new techniques are applied: KDDIAE, application of (Knowledge Discovery in Databases) to the Data of the Interaction between Agents and Students – Estudiantes in Spanish, and BIDAE (use of Data Analytics to obtain information of agents and students). The use of KDDIAE and BIDAE proves the existence of a fruitful relationship between learning analytics and learning design. Some samples of rules related to learning analytics and design are the following: (Learning Analytics) Children who initially do not know how to solve the exercise, after receiving help, are able to understand and solve it à (Learning Design) An agent for small children should be able to provide help. In addition, help should be entertaining and tailored to their characteristics because it is a resource that children actually use; or (Learning Analytics) Younger children use more voice interaction à (Learning Design) An agent interface for young children must incorporate voice commands. A complete list of rules related to learning analytics and design is provided for any researcher interested in PCA design. 72 children were able to use the new Dr. Roland after applying the learning analytics-design rules. They reported a 100 % satisfaction as they all enjoyed interacting with the agent.

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Huang, Yafang, Jinling Tang, Wilson Wai-san Tam, Chen Mao, Jinqiu Yuan, Mengyang Di, and Zuyao Yang. "Comparing the Overall Result and Interaction in Aggregate Data Meta-Analysis and Individual Patient Data Meta-Analysis." Medicine 95, no. 14 (April 2016): e3312. http://dx.doi.org/10.1097/md.0000000000003312.

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Su, Bo, Ruo Jun Qian, and Xiang Ke Han. "Study on Data Transfer Methods for Fluid-Structure Interaction Analysis." Advanced Materials Research 255-260 (May 2011): 3579–83. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.3579.

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The data transfer method for fluid structure interaction analysis using compactly supported radial based function (CRBF-FSI) is studied. It builds transfer matrix for data exchange and makes fluid and structure mesh use different shape and density unrestrictedly. Example of data exchange on 3D interface is studied. The efficient and the accurate of CRBF-FSI method are analyzed and also the influence of different compactly-supported radius is studied. The results show that CRBF-FSI method is suitable for FSI data transfer on complicated interface if compactly-supported radius is properly chosen. It has a bright future in practical use such as wind-induced response analysis in Wind Engineering.

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Amant, Robert St, and Paul R. Cohen. "Interaction with a mixed-initiative system for exploratory data analysis." Knowledge-Based Systems 10, no. 5 (March 1998): 265–73. http://dx.doi.org/10.1016/s0950-7051(97)00038-5.

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Phenix, Hilary, Katy Morin, Cory Batenchuk, Jacob Parker, Vida Abedi, Liu Yang, Lioudmila Tepliakova, Theodore J. Perkins, and Mads Kærn. "Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data." PLoS Computational Biology 7, no. 5 (May 12, 2011): e1002048. http://dx.doi.org/10.1371/journal.pcbi.1002048.

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Yan, Xiaobo, and Yichen Wang. "Fault Analysis in Software with the Data Interaction of Classes." International Journal of Security and Its Applications 9, no. 9 (September 30, 2015): 189–96. http://dx.doi.org/10.14257/ijsia.2015.9.9.17.

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Tao, Wang. "The Design of the Mobile SMS Data Analysis Interaction Platform." Physics Procedia 25 (2012): 848–52. http://dx.doi.org/10.1016/j.phpro.2012.03.167.

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Alice Uzuke, Chinwendu. "Two Factor Data Analysis with Unequal Cell Frequencies and Interaction." Science Journal of Applied Mathematics and Statistics 3, no. 6 (2015): 288. http://dx.doi.org/10.11648/j.sjams.20150306.18.

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Xiang, Zuoshuang, Yuying Tian, and Yongqun He. "PHIDIAS: a pathogen-host interaction data integration and analysis system." Genome Biology 8, no. 7 (2007): R150. http://dx.doi.org/10.1186/gb-2007-8-7-r150.

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Self, Jessica Zeitz, Michelle Dowling, John Wenskovitch, Ian Crandell, Ming Wang, Leanna House, Scotland Leman, and Chris North. "Observation-Level and Parametric Interaction for High-Dimensional Data Analysis." ACM Transactions on Interactive Intelligent Systems 8, no. 2 (July 14, 2018): 1–36. http://dx.doi.org/10.1145/3158230.

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Elsas, Donald A. "The Scheiblechner model: A loglinear analysis of social interaction data." Social Networks 12, no. 1 (March 1990): 57–82. http://dx.doi.org/10.1016/0378-8733(90)90022-2.

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Rekaya, Romdhane, and Kelly Robbins. "Ant colony algorithm for analysis of gene interaction in high-dimensional association data." Revista Brasileira de Zootecnia 38, spe (July 2009): 93–97. http://dx.doi.org/10.1590/s1516-35982009001300011.

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In recent years there has been much focus on the use of single nucleotide polymorphism (SNP) fine genome mapping to identify causative mutations for traits of interest; however, many studies focus only on the marginal effects of markers, ignoring potential gene interactions. Simulation studies have show that this approach may not be powerful enough to detect important loci when gene interactions are present. While several studies have examined potential gene interaction, they tend to focus on a small number of SNP markers. Given the prohibitive computation cost of modeling interactions in studies involving a large number SNP, methods need to be develop that can account for potential gene interactions in a computationally efficient manner. This study adopts a machine learning approach by adapting the ant colony optimization algorithm (ACA), coupled with logistic regression on haplotypes and genotypes, for association studies involving large numbers of SNP markers. The proposed method is compared to haplotype analysis, implemented using a sliding window (SW/H), and single locus genotype association (RG). Each algorithm was evaluated using a binary trait simulated using an epistatic model and HapMap ENCODE genotype data. Results show that the ACA outperformed SW/H and RG under all simulation scenarios, yielding substantial increases in power to detect genomic regions associated with the simulated trait.

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Yashin, Anatoliy, Dequing Wu, Konstantin Arbeev, Eric Stallard, Qihua Tan, Alexander Kulminski, Mary Feitosa, and Svetlana Ukraintseva. "Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)." Innovation in Aging 4, Supplement_1 (December 1, 2020): 491. http://dx.doi.org/10.1093/geroni/igaa057.1589.

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Abstract Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Uncovering hidden patterns of such interactions from human data will help better understand the nature of AD heterogeneity and find new targets for AD prevention. In this paper, we applied a newly developed method of evaluating joint GxG effects on AD to analysis of the Long Life Family Study data. The analysis included several steps: (i) selecting candidate genes from stress response pathways that are thought to be involved in AD; (ii) estimating interaction effects of SNP-pairs on AD risk, and selecting the top interacting SNPs; (iii) running GWAS-like interaction analysis for SNP-pairs, with one SNP fixed; (iv) using characteristics of the detected SNP-pairs interactions to construct the SNP-specific Interaction Polygenic Risk Scores (IPRS); and (v) evaluating the effects of IPRSs on AD. We found that SNP-specific IPRS have highly significant effects on AD risk. For most SNPs involved in the significant interaction effects on AD, their individual effects were statistically not significant. Male and female analyses yielded different subsets of the top interacting SNPs. These results support major role of genetic interactions in heterogeneity of AD, and indicate that AD mechanisms can involve different combinations of the interacting genetic variants in males and females, which may point to different pathways of resistance/response to stressors in two genders.

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Alawi, Malik, Stefan Kurtz, and Michael Beckstette. "CASSys: an integrated software-system for the interactive analysis of ChIP-seq data." Journal of Integrative Bioinformatics 8, no. 2 (June 1, 2011): 1–13. http://dx.doi.org/10.1515/jib-2011-155.

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Summary The mapping of DNA-protein interactions is crucial for a full understanding of transcriptional regulation. Chromatin-immunoprecipitation followed bymassively parallel sequencing (ChIP-seq) has become the standard technique for analyzing these interactions on a genome-wide scale. We have developed a software system called CASSys (ChIP-seq data Analysis Software System) spanning all steps of ChIP-seq data analysis. It supersedes the laborious application of several single command line tools. CASSys provides functionality ranging from quality assessment and -control of short reads, over the mapping of reads against a reference genome (readmapping) and the detection of enriched regions (peakdetection) to various follow-up analyses. The latter are accessible via a state-of-the-art web interface and can be performed interactively by the user. The follow-up analyses allow for flexible user defined association of putative interaction sites with genes, visualization of their genomic context with an integrated genome browser, the detection of putative binding motifs, the identification of over-represented Gene Ontology-terms, pathway analysis and the visualization of interaction networks. The system is client-server based, accessible via a web browser and does not require any software installation on the client side. To demonstrate CASSys’s functionality we used the system for the complete data analysis of a publicly available Chip-seq study that investigated the role of the transcription factor estrogen receptor-α in breast cancer cells.

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KURNIASARI, CECILIA INDRI. "Social Interaction on Patients with Schizophrenia in Psychiatric Hospital." Jurnal Ilmiah Kesehatan Keperawatan 15, no. 2 (January 15, 2020): 25. http://dx.doi.org/10.26753/jikk.v15i2.335.

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Social interaction is one of important indicator in the recovery process of mental patients, especially in patients with schizophrenia. Active social interactions can help patients with schizophrenia to socialize, while less active social interactions can cause social isolation to the risk of suicide. The purpose of this study was to determine the social interaction of patients with schizophrenia in psychiatric hospital. The number of participant in this study were 52 patients. Sampling with a purposive sampling technique. Data were collected using Social Interaction Questionnaire and Behavior Observation Sheet consisting of 18 statements. The analysis of this study was using univariate analysis with table of frequency distribution. The results showed that social interactions in schizophrenia patients were 45 patients with less active interacting categories, 5 patients with moderately active interacting categories, and 2 patients with active interacting categories. The results of the study can be used as a reference in determining appropriate nursing therapy in increasing social interaction in schizophrenia patients in mental hospitalsKeywords: social interaction; social psychological factors; schizophrenia;

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Shalehoddin, Shalehoddin, and Erwin Ashari. "“MOVE” ANALYSIS IN CLASSROOM INTERACTION (An Functional Grammar Approach." ANGLO-SAXON: Jurnal Ilmiah Program Studi Pendidikan Bahasa Inggris 6, no. 2 (May 1, 2016): 73. http://dx.doi.org/10.33373/anglo.v7i1.512.

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Find out the form of “move” in students classssroom interaction of UNRIKA during teaching practice program was the aim of this study. Conversation texts taken froms tudents’ classssroom interaction of UNRIKA during teaching practice program were the data of this research. To analize the data, in this study , descriptive qualitative method was used. There were five classroom interactions selected as the data source. The data were taken by recording then transcripted. Rewriting, Identifing utterences, elaborating and then analized were the process of data analysi. The findings of this study showed that were 261 clauses analysis. There were two types of move” in students classssroom interaction found, namely congruent and metaphorical “move” coding. Congruent and metaphorical “move” coding was 76.63% and metaphorical “move” coding was 27.37%. Congruent “move” coding was built in “knower” and “actor” analysis, but “knower” analysis was more dominant. It means the conversation texts dominnated by the statement and question, in another word, the content of the interaction refered to the lecturing and discussion situation. Keywords: “Move”,” Knower”, “Actor” Congruent and Metaphor Coding.

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Kang, D., W. D. Apel, J. C. Arteaga-Velázquez, K. Bekk, M. Bertaina, J. Blümer, H. Bozdog, et al. "Recent results from the KASCADE-Grande data analysis." EPJ Web of Conferences 208 (2019): 04005. http://dx.doi.org/10.1051/epjconf/201920804005.

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KASCADE, together with its extension KASCADE-Grande measured individual air showers of cosmic rays in the primary energy range of 100 TeV to 1 EeV. The data collection was fully completed at the end of 2013 and the experiment was dismantled. However, the data analysis is still in progress. Recently, we published a new result on upper limits to the flux of ultra-high energy gamma rays, which set constraints on some fundamental astrophysical models. We also use the data to investigate the validity of the new hadronic interactions models like SIBYLL version 2.3c or EPOS-LHC. In addition, we updated and improved the webbased platform of the KASCADE Cosmic Ray Data Centre (KCDC), where now the data from KASCADE and KASCADE-Grande of more than 20 years measurements is available, including corresponding Monte-Carlo simulated events based on three different hadronic interaction models. In this contribution, recent results from KASCADE-Grande and the update of KCDC is briefly discussed.

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Kataev, M. Yu, and V. V. Orlova. "Social media event data analysis." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 4 (December 25, 2020): 71–77. http://dx.doi.org/10.21293/1818-0442-2020-23-4-71-77.

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Social media analysis has become ubiquitous at a quantitative and qualitative level due to the ability to study content from open social networks. This content is a rich source of data for the construction and analysis of the interaction of social network users when forming various groups, used not only for statistical calculations, social areas of analysis, but also in trade or for the development of recommendation systems. The large number of social media users results in a huge amount of unstructured data (by time, type of communication, type of message and geographic location). This article aims to discuss the problem of analyzing social networks and obtaining information from unstructured data. The article discusses information extraction methods, well-known software products and datasets.

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Benyon, David. "Task analysis and system design: the discipline of data." Interacting with Computers 4, no. 2 (August 1992): 246–59. http://dx.doi.org/10.1016/0953-5438(92)90008-4.

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Sizemore, Ann E., Jennifer E. Phillips-Cremins, Robert Ghrist, and Danielle S. Bassett. "The importance of the whole: Topological data analysis for the network neuroscientist." Network Neuroscience 3, no. 3 (January 2019): 656–73. http://dx.doi.org/10.1162/netn_a_00073.

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Data analysis techniques from network science have fundamentally improved our understanding of neural systems and the complex behaviors that they support. Yet the restriction of network techniques to the study of pairwise interactions prevents us from taking into account intrinsic topological features such as cavities that may be crucial for system function. To detect and quantify these topological features, we must turn to algebro-topological methods that encode data as a simplicial complex built from sets of interacting nodes called simplices. We then use the relations between simplices to expose cavities within the complex, thereby summarizing its topological features. Here we provide an introduction to persistent homology, a fundamental method from applied topology that builds a global descriptor of system structure by chronicling the evolution of cavities as we move through a combinatorial object such as a weighted network. We detail the mathematics and perform demonstrative calculations on the mouse structural connectome, synapses in C. elegans, and genomic interaction data. Finally, we suggest avenues for future work and highlight new advances in mathematics ready for use in neural systems.

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Ronan, Tom, Roman Garnett, and Kristen M. Naegle. "New analysis pipeline for high-throughput domain–peptide affinity experiments improves SH2 interaction data." Journal of Biological Chemistry 295, no. 32 (June 15, 2020): 11346–63. http://dx.doi.org/10.1074/jbc.ra120.012503.

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Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2–pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2–pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2–pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2–pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain–peptide interactions.

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Journal articles on the topic 'Data interaction analysis'

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