A Molecular Biology Database Digest

Computational Biology or Bioinformatics has been defined as the application of mathematical and Computer Science methods to solving problems in Molecular Biology that require large scale data, computation, and analysis [18]. As expected, Molecular Biology databases play an essential role in Computational Biology research and development. This paper introduces into current Molecular Biology databases, stressing data modeling, data acquisition, data retrieval, and the integration of Molecular Biology data from different sources. This paper is primarily intended for an audience of computer scientists with a limited background in Biology

COLOMBOS v2.0 : an ever expanding collection of bacterial expression compendia

The COLOMBOS database (http://www.colombos.net) features comprehensive organism-specific cross-platform gene expression compendia of several bacterial model organisms and is supported by a fully interactive web portal and an extensive web API. COLOMBOS was originally published in PLoS One, and COLOMBOS v2.0 includes both an update of the expression data, by expanding the previously available compendia and by adding compendia for several new species, and an update of the surrounding functionality, with improved search and visualization options and novel tools for programmatic access to the database. The scope of the database has also been extended to incorporate RNA-seq data in our compendia by a dedicated analysis pipeline. We demonstrate the validity and robustness of this approach by comparing the same RNA samples measured in parallel using both microarrays and RNA-seq. As far as we know, COLOMBOS currently hosts the largest homogenized gene expression compendia available for seven bacterial model organisms

ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Mixed membership stochastic blockmodels

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.Comment: 46 pages, 14 figures, 3 table

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

Gene-environment correlation linking aggression and peer victimization : do classroom behavioral norms matter?

Using a genetically informed design based on 197 Monozygotic and Dizygotic twin pairs assessed in grade 4, this study examined 1) whether, in line with a gene-environment correlation (rGE), a genetic disposition for physical aggression or relational aggression puts children at risk of being victimized by their classmates, and 2) whether this rGE is moderated by classroom injunctive norm salience in regard to physical or relational aggression. Physical aggression and relational aggression, as well as injunctive classroom norm salience in regard to these behaviors, were measured via peer nominations. Peer victimization was measured via self-reports. Multi-Level Mixed modeling revealed that children with a genetic disposition for either aggressive behavior are at higher risk of being victimized by their peers only when classroom norms are unfavourable toward such behaviors. However, when classroom injunctive norms favor aggressive behaviors, a genetic disposition for physical or relational aggression may actually protect children against peer victimization. These results lend further support to the notion that bullying interventions must include the larger peer context instead of a sole focus on victims and bullies

Bibliometric Perspectives on Medical Innovation using the Medical Subject Headings (MeSH) of PubMed

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the Medline database. Focusing on three main branches-"diseases," "drugs and chemicals," and "techniques and equipment"-we use base maps and overlay techniques to investigate the translations and interactions and thus to gain a bibliometric perspective on the dynamics of medical innovations. To this end, we first analyze the Medline database, the MeSH index tree, and the various options for a static mapping from different perspectives and at different levels of aggregation. Following a specific innovation (RNA interference) over time, the notion of a trajectory which leaves a signature in the database is elaborated. Can the detailed index terms describing the dynamics of research be used to predict the diffusion dynamics of research results? Possibilities are specified for further integration between the Medline database, on the one hand, and the Science Citation Index and Scopus (containing citation information), on the other.Comment: forthcoming in the Journal of the American Society for Information Science and Technolog