Dataset Information

Phenoclustering: online mining of cross-species phenotypes.

ABSTRACT:

Summary

Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information.

Availability

Freely available at http://www.phenomicdb.de

SUBMITTER: Groth P

PROVIDER: S-EPMC2905556 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Publications

Phenoclustering: online mining of cross-species phenotypes.

Groth Philip P Kalev Ivan I Kirov Ivaylo I Traikov Borislav B Leser Ulf U Weiss Bertram B

Bioinformatics (Oxford, England) 20100618 15

<h4>Summary</h4>Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional ana ...[more]

PMID: 20562418

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Project description:Background and objectivesThe Internet provides easy access to multiple types of gambling and has led to changes in betting habits. A severe rise in problematic gambling has been predicted among all sectors of the population, and studies are required to assess the emerging phenotypes related to the new structures of gambling activities. This study aimed to explore the existence of latent classes associated with gambling habits among treatment-seeking gamblers due to Online Sports Betting (OSB).MethodInitial sample included n = 4,516 patients consecutively admitted for treatment in a hospital unit specialized in behavioral addictions. Two-step clustering analysis was used within the subsample of n = 323 patients who reported problems related with OSB, within a set of indicators including sociodemographics, psychopathological distress, personality, and severity of the gambling activity.ResultsThe prevalence of OSB as a main type of gambling problem in the study was 7.2% (95% confidence interval: 6.4 to 7.9%). Two latent clusters were identified, with differences in sociodemographics and clinical status. Cluster 1 (n = 247, 76.5%) grouped patients that were more affected due to the OSB behaviors, and it was characterized by non-married patients, lower socioeconomic position index, higher comorbidity with other substance related addictions, younger age, and early onset of the gambling activity, as well as higher debts due to the OSB, higher psychopathological distress, and a more dysfunctional personality profile. Cluster 2 (n = 76, 23.5%) grouped patients that were less affected by OSB, mostly married (or living with a stable partner), with higher social position levels, older age and older onset of the gambling activity, as well as a more functional psychopathological and personality profile.ConclusionThe increasing understanding of latent classes underlying OSB phenotypes is essential in guiding the development of reliable screening tools to identify individuals highly vulnerable to addictive behaviors among Internet gamblers, as well as in planning prevention and treatment initiatives focused on the precise profiles of these patients.

Project description:To successfully learn using open Internet resources, students must be able to critically search, evaluate and select online information, and verify sources. Defined as critical online reasoning (COR), this construct is operationalized on two levels in our study: (1) the student level using the newly developed Critical Online Reasoning Assessment (CORA), and (2) the online information processing level using event log data, including gaze durations and fixations. The written responses of 32 students for one CORA task were scored by three independent raters. The resulting score was operationalized as "task performance," whereas the gaze fixations and durations were defined as indicators of "process performance." Following a person-oriented approach, we conducted a process mining (PM) analysis, as well as a latent class analysis (LCA) to test whether-following the dual-process theory-the undergraduates could be distinguished into two groups based on both their process and task performance. Using PM, the process performance of all 32 students was visualized and compared, indicating two distinct response process patterns. One group of students (11), defined as "strategic information processers," processed online information more comprehensively, as well as more efficiently, which was also reflected in their higher task scores. In contrast, the distributions of the process performance variables for the other group (21), defined as "avoidance information processers," indicated a poorer process performance, which was also reflected in their lower task scores. In the LCA, where two student groups were empirically distinguished by combining the process performance indicators and the task score as a joint discriminant criterion, we confirmed these two COR profiles, which were reflected in high vs. low process and task performances. The estimated parameters indicated that high-performing students were significantly more efficient at conducting strategic information processing, as reflected in their higher process performance. These findings are so far based on quantitative analyses using event log data. To enable a more differentiated analysis of students' visual attention dynamics, more in-depth qualitative research of the identified student profiles in terms of COR will be required.

Dataset Information

Phenoclustering: online mining of cross-species phenotypes.

Summary

Availability

Publications

Phenoclustering: online mining of cross-species phenotypes.

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