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PSFC is implemented in Java and is available as an app for Cytoscape 3. The main module consists of two main packages, logic and gui.
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The package logic is designed to handle PSFC-inherent structures and algorithms, while the gui package is responsible for user communication via the PSFC tab in the Cytoscape GUI west panel, and for mapping Cytoscape inherent data structures to PSFC data structures (Graph, Node and Edge) contained in the logic package.
The concept of packaging application logic and resource management logic as a holistic resource container is implemented to perform large biological microarray data analysis.
One of our methods then reduces the problem of determining the existence of Hopf bifurcation fixed points to a first-order formula over the ordered field of the reals that can be solved using computational logic packages.
The model was constructed using the Any Logic software package (http://www.xjtek.com/anylogic/).com/anylogic/
The choice of packaging follows this logic: the stereo has expanded booklets with rare photographs while the mono has the original album art shrunk down to CD size and original LP labels replicated on the disks.
The VHDL-2008 versiof of the package defines array/scalar logic operations for std_ulogic and std_ulogic_vector values, mirroring those that are predefined for bit/bit_vector and boolean/boolean_vector.
Following this, the key failure mechanisms at the front end, back end, and package level for logic technology and their driving forces are discussed in-depth along with a brief overview of degradation metrics in nonvolatile memory devices as well.
The design of this package follows the logic that gene expression profiles contain biologically relevant signatures; gene expression signatures are predictable and complement for understanding biological processes.
It also presents the corresponding software package developed for control logic creation.
Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data.
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