KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species
KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species
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KnetMiner:支持跨物种基于证据的基因发现和复杂性状分析的综合方法
DOI:
10.1101/2020.04.02.017004
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发表时间:
2020
期刊:
影响因子:
--
通讯作者:
Hassani-Pak K
中科院分区:
文献类型:
--
作者:
Hassani-Pak K
Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists’ own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting inTriticum aestivum, as well as, an evidence-based approach to identify candidate genes under anArabidopsis thalianapetal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org.
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DOI:
--
发表时间:
2020
期刊:
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作者:
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DOI:
--
发表时间:
2017
期刊:
Workshop on Semantic Web Applications and Tools for Life Sciences
影响因子:
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L. García;O. Giraldo;A. G. Castro;M. Dumontier;Bioschemas Community
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Bioschemas Community
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Stephens ZD;Lee SY;Faghri F;Campbell RH;Zhai C;Efron MJ;Iyer R;Schatz MC;Sinha S;Robinson GE
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