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Node perturbation study of Protein-Protein Interaction
Network VIJEESH T
M120443CSGUIDED By: Jayaraj P B
Problem Statement
Device a parallel method to calculate centrality measures of proteins in a
protein interaction network and implement on GPU. Study the effect of
removing a protein from a protein interaction network on these measure.
Centrality measures• Identify the importance of a node in a graph
• E.g. Degree, closeness and betweenness
• Centrality measures can be used to rank proteins according to their essentiality. This helps to identify lethality proteins and drug targets. [1]
Inverse of the sum of distance to all other nodes – closeness[3]
Fraction of shortest paths between other nodes –betweenness[2]
Plan of Actions
Identify a parallel algorithm to compute All-Pair-Shortest Path on Graph Develop a parallel method to compute betweenness centrality Identify sparse graph storage representation Implement on GPU and compare performance with CPU
• Identify a PIN corresponding to a particular disease
• Find the relationship between important proteins and disease causing proteins.
Work done before last evaluation
• Identified parallel algorithm to compute All Pair Shortest Path.• Delta-stepping algorithm [5]
• Identified faster algorithm to compute betweenness centrality[4]
• Modified delta-stepping algorithm to compute betweenness centrality
• Implemented Delta-stepping algorithm on GPU
• Identified sparse matrix storage representation –CSR [6]
Work done after last evaluation
• Implemented betweenness centrality on GPU
Challenges• Storage of predecessors information.
• Lack of stack data structure.
Faster algorithm for BC
• Instead of computing pair-dependency, compute the dependency of vertex s on a vertex v
• Brandes[4] algorithm give recursive equation for dependency dep(u) = *(1 + dep(v))
The Modified algorithm
• For each source in V• Scan the edges for relaxations
• During relaxation, update predecessor, sigma and level
• For each edge in parallel update the dependency [4]
dep(u) = dep(u) + *(1 + dep(v))
• For each vertex in parallel update the centrality in bottom-up manner
Results• Implemented betweenness centrality and closeness centrality on GPU
• Will work for both weighted and unweighted graphs.
facebook yeast PIN0
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CPU GPU
Future work
• Identification of PIN data
• Include other metric like influential spreading
• Identify the relation between centrality values and disease causing proteins.
References[1] H. Jeong, S. P. Mason, A. L. Barabasi, and Z. N. Oltvai, “Lethality and centrality in protein
networks," Nature, vol. 411, no. 6833, pp.41-42, May 2001.
[2] L. C. Freeman, “A set of measures of centrality based on betweenness," Sociometry, vol. 40, no. 1, pp. 35-41, March 1977.
[3] G. Sabidussi, “The centrality index of a graph," Psychometrika, vol. 31, no. 4, pp. 581-603, 1966.
[4] U. Brandes, “A faster algorithm for betweenness centrality," Journal of Mathematical Sociology, vol. 25, pp. 163-177, 2001.
[5] U. Meyer and P. Sanders, “Delta-stepping: a parallelizable shortest path algorithm," Journal of Algorithms, vol. 49, no. 1, pp. 114 -152, 2003
[6] N. Bell and M. Garland, “Efficient sparse matrix-vector multiplication on CUDA," NVIDIA Corporation, NVIDIA Technical Report NVR-2008-004, Dec. 2008.
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