1,721,086 research outputs found
A collaborative web application for supporting researchers in the task of generating protein datasets
No full textThe huge difference between known sequences and known tertiary structures has fostered the development of automated methods and systems for protein analysis.When these systems are learned using machine learning techniques, the capability of training them with suitable data becomes of paramount importance. From this perspective, the search for (and the generation of) specialized datasets that meet specific requirements are prominent activities for researchers. To help researchers in these activities we developed ProDaMa-C, a web application aimed at generating specialized protein structure datasets and fostering the collaboration among researchers. ProDaMa-C provides a collaborative environmentwhere researcherswith similar interests can meet and collaborate to generate new datasets. Datasets are generated selecting proteins through user-defined pipelines of methods/operators. Each pipeline can also be used as starting point for building further pipelines able to enforce additional selection criteria. Freely available as web application at the URL http://iasc.diee.unica.it/prodamac , ProDaMa-C has shown to be a useful tool for researchers involved in the task of generating specialized protein structure datasets
PRODAMA-C: A COLLABORATIVE WEB APPLICATION TO GENERATE SPECIALIZED PROTEIN STRUCTURE DATASETS
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Limitations in the use of Sentinel-1 data for morphological change detection in rivers
Full text linkThe identification of morphological changes occurring along river channels is essential to support river process understanding, assess sediment budgets and evaluate the effectiveness of river management. Among available remote sensing techniques, space-borne synthetic aperture radar (SAR) could potentially provide a powerful complement to optical imagery for this task. However, very few studies have been carried out on the use of SAR datasets to study erosion and deposition processes in river channels. In this work, we investigate the potential of change detection analysis based on Sentinel-1 data, by comparing variations of radar backscattering to river morphology changes identified through high-resolution drone acquisitions. We considered a time series of two years of Sentinel-1 data relative to a period where, despite a moderate fluvial event occurred, morphological changes have been significantly detected in multitemporal drone point clouds. Satellite optical imagery (planet.com) and hydro-meteorological data were used to support the analysis and interpret results. The results show that the spatial and temporal resolution of Sentinel-1 is currently not suitable for accurate discrimination of morphological changes related to river dynamics at local scale. Other spaceborne sensors with sub-metric ground sampling distance and/or daily revisit time would be probably suitable; however, so far, this option would need the use of commercial solutions with a consistent increase of the costs of the investigation
ProDaMa: an open source Python library to generate protein structure datasets
No full textAbstract Background The huge difference between the number of known sequences and known tertiary structures has justified the use of automated methods for protein analysis. Although a general methodology to solve these problems has not been yet devised, researchers are engaged in developing more accurate techniques and algorithms whose training plays a relevant role in determining their performance. From this perspective, particular importance is given to the training data used in experiments, and researchers are often engaged in the generation of specialized datasets that meet their requirements. Findings To facilitate the task of generating specialized datasets we devised and implemented ProDaMa, an open source Python library than provides classes for retrieving, organizing, updating, analyzing, and filtering protein data. Conclusion ProDaMa has been used to generate specialized datasets useful for secondary structure prediction and to develop a collaborative web application aimed at generating and sharing protein structure datasets. The library, the related database, and the documentation are freely available at the URL http://iasc.diee.unica.it/prodama.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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