Add to ORKGPresented at: International Symposium on Integrative Bioinformatics (10th : 2014), 12th - 14th May 2014. Newcastle University, U
BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An i...
Computational drug repositioning approaches are important, as they cost less com-pared to the tradit...
Identification of meaningful chemical patterns in today´s increasing amounts of high throughput gene...
Current research and development approaches to drug discovery have become less fruitful and more cos...
Abstract Searching for interesting common subgraphs in graph data is a well-studied problem in data ...
Drug repositioning can reduce the time, costs and risks of drug development by identifying new thera...
Drug development is both increasing in cost whilst decreasing in productivity. There is a general ac...
We present a data integration and data mining platform, called BioGraph, for knowledge discovery in ...
Motivations. The graph is a data structure to represent biological data ranging from molecules and p...
The analysis of ‘Big Data’ has great potential in drug discovery; however complications arise in int...
The rapid accumulation of biological network data is creating an urgent need for computational metho...
The search for frequent subgraphs is becoming increasingly important in many application areas incl...
Drug repositioning is a useful way to discover new drug candidates for curing diseases. However, int...
We present a method for finding biologically meaning-ful patterns on metabolic pathways using the SU...
Abstract Background Diverse interactions occur between biomolecules, such as activation, inhibition,...
BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An i...
Computational drug repositioning approaches are important, as they cost less com-pared to the tradit...
Identification of meaningful chemical patterns in today´s increasing amounts of high throughput gene...
Current research and development approaches to drug discovery have become less fruitful and more cos...
Abstract Searching for interesting common subgraphs in graph data is a well-studied problem in data ...
Drug repositioning can reduce the time, costs and risks of drug development by identifying new thera...
Drug development is both increasing in cost whilst decreasing in productivity. There is a general ac...
We present a data integration and data mining platform, called BioGraph, for knowledge discovery in ...
Motivations. The graph is a data structure to represent biological data ranging from molecules and p...
The analysis of ‘Big Data’ has great potential in drug discovery; however complications arise in int...
The rapid accumulation of biological network data is creating an urgent need for computational metho...
The search for frequent subgraphs is becoming increasingly important in many application areas incl...
Drug repositioning is a useful way to discover new drug candidates for curing diseases. However, int...
We present a method for finding biologically meaning-ful patterns on metabolic pathways using the SU...
Abstract Background Diverse interactions occur between biomolecules, such as activation, inhibition,...
BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An i...
Computational drug repositioning approaches are important, as they cost less com-pared to the tradit...
Identification of meaningful chemical patterns in today´s increasing amounts of high throughput gene...