#Nvidia gtx 680 part number 2002 0000 software
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the manuscript and its Supporting Information files.įunding: YKK: This work was supported by National IT Industry Promotion Agency (NIPA) grant funded by the Korea government (MSIP) (S02, Development of smart RMS software for ship maintenance-based fault predictive diagnostics)( ). Received: AugAccepted: FebruPublished: March 19, 2019Ĭopyright: © 2019 Trinh, Kwon. Mombach, Universidade Federal de Santa Maria, BRAZIL Ĭitation: Trinh H-C, Kwon Y-K (2019) RMut: R package for a Boolean sensitivity analysis against various types of mutations. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively.
RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In addition, RMut can specify the mutation area and the duration time for more precise analysis. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. You can search for the right part based on make, model, year and other related fields.There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations.
#Nvidia gtx 680 part number 2002 0000 pro
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