Netter: re-ranking gene network inference predictions using structural network properties
نویسندگان
چکیده
منابع مشابه
Improving gene regulatory network inference using network topology information.
Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well kn...
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GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data properties.
A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method. We present GeneSPIDER - a Matlab package for tuning, running, and evaluating inference algorithms that allows independent control of network and data properties to enable data-driven benchmarking. GeneSPIDER is uniquely suited to address...
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It has been attempted to reveal regulatory information from microarray data using Bayesian network [1]. However, due to limitation of microarray, successful result is obtained only under a limited condition. For this reason, Bayesian network from combining microarray with biological knowledge was proposed [2]. In this paper, we proposed Bayesian network learned by genetic algorithm to infer gen...
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Bayesian Networks (BNs) have become one of the most powerful means of reconstructing signalling pathways in silico. Excessive computational loads limit the applications of BNs to learn larger sized network structures. Recent bioinformatics research found that signalling pathways are likely hierarchically organised. Genes resident in hierarchical layers constitute biological constraint, which ca...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2016
ISSN: 1471-2105
DOI: 10.1186/s12859-016-0913-0