Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors

Dragan Bosnacki, Maximilian R Odenbrett, Anton Wijs, Willem Ligtenberg, Peter Hilbers
Department of Biomedical Engineering, Eindhoven University of Technology, PO Box, 513, 5600 MB, Eindhoven, The Netherlands
BMC Bioinformatics, 13:281, 2012

   title={Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors},

   author={Bo{v{s}}nacki, D. and Odenbrett, M.R. and Wijs, A. and Ligtenberg, W. and Hilbers, P.},

   journal={BMC bioinformatics},





Download Download (PDF)   View View   Source Source   Source codes Source codes




BACKGROUND: Techniques for reconstruction of biological networks which are based on perturbation experimentsoften predict direct interactions between nodes that do not exist. Transitive reduction removes suchrelations if they can be explained by an indirect path of in influences. The existing algorithms fortransitive reduction are sequential and might suffer from too long run times for large networks. Theyalso exhibit the anomaly that some existing direct interactions are also removed. RESULTS: We develop efficient scalable parallel algorithms for transitive reduction on general purpose graphicsprocessing units for both standard (unweighted) and weighted graphs. Edge weights are regarded asuncertainties of interactions. A direct interaction is removed only if there exists an indirectinteraction path between the same nodes which is strictly more certain than the direct one. This is arefinement of the removal condition for the unweighted graphs and avoids to a great extent theerroneous elimination of direct edges. CONCLUSIONS: Parallel implementations of these algorithms can achieve speed-ups of two orders of magnitudecompared to their sequential counterparts. Our experiments show that: i) taking into account theedge weights improves the reconstruction quality compared to the unweighted case; ii) it isadvantageous not to distinguish between positive and negative interactions since this lowers thecomplexity of the algorithms from NP-complete to polynomial without loss of quality.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1666 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

339 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: