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Rene Ranzinger

Publications and source records attributed to Rene Ranzinger.

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Engaging the scientific community in high-quality biocuration: a report on the International Society for Biocuration workshop, 'Maximizing community curation for the benefit of all'

Biological knowledgebases traditionally rely on expert, professional curation of the research literature to maintain up-to-date collections of data organized in machine-readable form. However, despite the increasing amount of curatable biomedical knowledge, support for knowledgebases is declining, leaving these resources no alternative but to explore additional ways of updating and maintaining content. One way in which knowledgebases have addressed this problem is by engaging researchers to help curate their published papers, a process generally known as 'community curation'. As helpful as community curation can be, though, it is not universally adopted and, for groups that do have it, there is a wide range of approaches. To learn about existing community curation pipelines and explore possibilities for working towards a common approach, we organized a workshop, Maximizing Community Curation for the Benefit of All, at the 18th International Biocuration Conference, hosted by the Stowers Institute for Medical Research. Our aim was to examine the different strategies that groups use, share successes, failures, and ongoing challenges, and produce suggested deliverables for broader adoption of common best practices and tools for effective community curation. Representatives from 18 different resources, ranging from model organism and specialty knowledgebases to journals and literature resources, presented their work. The result was a comprehensive assessment of the state-of-the-art for community curation and an in-depth discussion on how community curation can become standard practice for maintaining timely, highquality biological resources that will continue to provide scientists with the essential information they need for their research.

cs.DB

GELATO and SAGE: An Integrated Framework for MS Annotation

Several algorithms and tools have been developed to (semi) automate the process of glycan identification by interpreting Mass Spectrometric data. However, each has limitations when annotating MSn data with thousands of MS spectra using uncurated public databases. Moreover, the existing tools are not designed to manage MSn data where n > 2. We propose a novel software package to automate the annotation of tandem MS data. This software consists of two major components. The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO). This tool extends and automates the functionality of existing open source projects, namely, GlycoWorkbench (GWB) and GlycomeDB. The second is a machine learning model called Smart Anotation Enhancement Graph (SAGE), which learns the behavior of glycoanalysts to select annotations generated by GELATO that emulate human interpretation of the spectra.

cs.AI

Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model

Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of the most common PGMs used in machine learning and data mining, demonstrates this limitation when the training data consists of random variables, each of them has a large set of possible values. In the big data era, one would expect new extensions to the existing PGMs to handle the massive amount of data produced these days by computers, sensors and other electronic devices. With hierarchical data - data that is arranged in a treelike structure with several levels - one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian Networks become infeasible for representing the probability distributions. In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space. The proposed model achieves perfect precision of 1.0 and high recall of 0.93 when it is used as multi-label classifier for the annotation of mass spectrometry data. On another data set of 1.5 billion search logs provided by CareerBuilder.com the model was able to predict latent semantic relationships between search keywords with accuracy up to 0.80.

cs.AI