Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, using these experimental methods, it is still challenging to identify temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this review, we summarize concepts and applications of these computational methods for inferring ...
Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, ste...
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of...
We propose dynamic graph-based relational mining approach to learn structural patterns in graphs or ...
Cells execute their functions through dynamic operations of biological networks. Dynamic networks de...
DoctorCells execute their functions through dynamic operations of biological networks. Dynamic netwo...
Biological networks are currently being studied with approaches derived from the mathematical and ph...
Biological networks are currently being studied with approaches derived from the mathematical and ph...
In this chapter, we review the problem of network inference from time-course data, focusing on a cla...
We consider relations of structure and dynamics in complex networks. Firstly, a dynamical perspectiv...
Motivation: Biological networks change in response to genetic and environmental cues. Changes are re...
Background: One of the most important projects in the post-genome-era is the systemic identification...
Many complex processes, from protein folding to neuronal network dynamics, can be described as stoch...
In this thesis, we consider three network-based systems, focusing on emergent behaviors resulting fr...
Dynamic gene-regulatory networks are complex since the interaction patterns between their components...
Networks observed in real world like social networks, collaboration networks etc., exhibit temporal ...
Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, ste...
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of...
We propose dynamic graph-based relational mining approach to learn structural patterns in graphs or ...
Cells execute their functions through dynamic operations of biological networks. Dynamic networks de...
DoctorCells execute their functions through dynamic operations of biological networks. Dynamic netwo...
Biological networks are currently being studied with approaches derived from the mathematical and ph...
Biological networks are currently being studied with approaches derived from the mathematical and ph...
In this chapter, we review the problem of network inference from time-course data, focusing on a cla...
We consider relations of structure and dynamics in complex networks. Firstly, a dynamical perspectiv...
Motivation: Biological networks change in response to genetic and environmental cues. Changes are re...
Background: One of the most important projects in the post-genome-era is the systemic identification...
Many complex processes, from protein folding to neuronal network dynamics, can be described as stoch...
In this thesis, we consider three network-based systems, focusing on emergent behaviors resulting fr...
Dynamic gene-regulatory networks are complex since the interaction patterns between their components...
Networks observed in real world like social networks, collaboration networks etc., exhibit temporal ...
Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, ste...
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of...
We propose dynamic graph-based relational mining approach to learn structural patterns in graphs or ...