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We conducted more than 30 in-depth interviews with experts in this area, attended conferences, analyzed protocols from the meetings of relevant networks, and examined the formation of specific agencies launched to promote the development of hydrogen and fuel cell technologies.
By comparing the imported microarray data with the IPKB, the list of genes was transformed in a set of relevant networks, focus genes and canonical pathways were identified, and functional annotation was performed.
The Ingenuity Pathways Analysis (IPA) is a system that transforms large data sets into a group of relevant networks containing direct and indirect relationships between genes based on known interactions in the literature.
In short, IPA was used to convert a list of differentially expressed genes into a set of relevant networks based on a continually updated database of known molecular pathways.
We performed Ingenuity Pathway Analysis (Ingenuity® Systems,, IPA 6.0) in order to find significant molecular functional categories in SAMD-target gene set and transform the target genes into a set of relevant networks by using literature-based records that are maintained in the Ingenuity Pathway Knowledge Base.
We performed IPA (version 6.0; Ingenuity® Systems, Redwood City, CA, USA) [ 36] to find significant molecular functional categories enriched in the differentially expressed gene sets, and transformed the target genes into a set of relevant networks by using literature-based records that are maintained in the IPA Base.
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A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics.
In each case, the MMSE detection algorithm at the AP has been derived and the impacts of relevant network parameters (e.g., the sensor SNR, the communication noise level, and the number of sensors) have been investigated.
Therefore, as a hypothesis building tool, transcript screens serve as valuable indicators of relevant network areas to study further.
A total of six relevant networks composed of 34 miRNAs were generated, with 14 miRNAs in Network 1, 12 in Network 2 and 2 miRNAs in each of networks 3, 4, 5, and 6.
Therefore, to reach high-confidence conclusions, we evaluated the merits of various network reconstruction algorithms over a wide range of biologically relevant networks.
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