84 research outputs found

    Sustainable conditions for the development of renewable energy systems : a triple bottom line perspective

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    Renewable energy systems (RES) have been proposed as an effective solution for sustainable development. However, the impact of municipal contextual conditions on the development of RES is still unclear. One of the literature gaps is the lack of understanding of whether the balanced development of economic, social, and environmental aspects of sustainability – the triple bottom line (TBL) perspective – can support RES policy. We conducted a quantitative analysis of 727 medium- and large-sized German municipalities to understand whether municipalities should create contextual conditions around the TBL dimensions to support RES policy. Furthermore, we applied a cluster analysis to establish the patterns of RES adoption supported by the TBL. Our results document that advanced adopters of RES are more advanced regarding the economic and environmental aspects of the TBL, and their RES development outperforms in the development of a knowledge-base and social cooperation. In contrast, regions with less RES development primarily emphasize reducing energy dependency and increasing social acceptance. As the main contribution, the study provides a novel view on how sustainability and RES development work together by providing details about the connection between specific TBL dimensions and elements with different maturity levels of RES policy implementation

    Algebraic Comparison of Partial Lists in Bioinformatics

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    The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

    Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

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    Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat

    DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks

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    Background Functional genomic and epigenomic research relies fundamentally on sequencing based methods like ChIP-seq for the detection of DNA-protein interactions. These techniques return large, high dimensional data sets with visually complex structures, such as multi-modal peaks extended over large genomic regions. Current tools for visualisation and data exploration represent and leverage these complex features only to a limited extent. Results We present DGW, an open source software package for simultaneous alignment and clustering of multiple epigenomic marks. DGW uses Dynamic Time Warping to adaptively rescale and align genomic distances which allows to group regions of interest with similar shapes, thereby capturing the structure of epigenomic marks. We demonstrate the effectiveness of the approach in a simulation study and on a real epigenomic data set from the ENCODE project. Conclusions Our results show that DGW automatically recognises and aligns important genomic features such as transcription start sites and splicing sites from histone marks. DGW is available as an open source Python package

    DataBase: Research and Evaluation Results

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