The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine

What the eye does not see: visualizations strategies for the data collection of personal networks

The graphic representation of relational data is one of the central elements of social network analysis. In this paper, the author describe the use of visualization in interview-based data collection procedures designed to obtain personal networks information, exploring four main contributions. First, the author shows a procedure by which the visualization is integrated with traditional name generators to facilitate obtaining information and reducing the burden of the interview process. Second, the author describes the reactions and qualitative interpretation of the interviewees when they are presented with an analytical visualization of their personal network. The most frequent strategies consist in identifying the key individuals, dividing the personal network in groups and classifying alters in concentric circles of relative importance. Next, the author explores how the visualization of groups in personal networks facilitates the enumeration of the communities in which individuals participate. This allows the author to reflect on the role of social circles in determining the structure of personal networks. Finally, the author compares the graphic representation obtained through spontaneous, hand-drawn sociograms with the analytical visualizations elicited through software tools. This allows the author to demonstrate that analytical procedures reveal aspects of the structure of personal networks that respondents are not aware of, as well as the advantages and disadvantages of using both modes of data collection. For this, the author presents findings from a study of highly skilled migrants living in Spain (n = 95) through which the author illustrates the challenges, in terms of data reliability, validity and burden on both the researcher and the participants

Automated Protein Structure Classification: A Survey

Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table