Method for quickly inferring the mechanisms of large-scale complex networks based on the census of subgraph concentrations

A method for reverse interactome analysi

A method for reverse interactome analysis: High-resolution mapping of interdomain interaction network in Dam1 complex and its specific disorganization based on the interaction domain expression

✍ Akinori Ikeuchi; Hideo Nakano; Takuma Kamiya; Tsuneo Yamane; Yasuaki Kawarasaki 📂 Article 📅 2010 🏛 American Institute of Chemical Engineers 🌐 English ⚖ 456 KB 👁 3 views

[ACM Press the 12th International Confer

[ACM Press the 12th International Conference - Paris, France (2010.11.08-2010.11.10)] Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services - iiWAS '10 - Tensor models for the efficient multipath routing in large-scale communication networks

✍ Strykhalyuk, B.; Kaidan, M.; Klymash, M.; Kryvinska, N. 📂 Article 📅 2010 🏛 ACM Press ⚖ 252 KB

[ACM Press the 12th International Confer

[ACM Press the 12th International Conference - Paris, France (2010.11.08-2010.11.10)] Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services - iiWAS '10 - Tensor models for the efficient multipath routing in large-scale communication networks

✍ Strykhalyuk, B.; Kaidan, M.; Klymash, M.; Kryvinska, N. 📂 Article 📅 2010 🏛 ACM Press ⚖ 252 KB

We focus in this paper onto the analysis of a multipath routing in the large-scale communication networks. Further, we propose and develop a methodology to increase efficiency of network performance with the multipath routing by usage of an analytical technique, e.g., tensor models. Particularly, we

[IEEE 2011 International Conference on T

[IEEE 2011 International Conference on Transportation and Mechanical & Electrical Engineering (TMEE) - ChangChun, China (2011.12.16-2011.12.18)] Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE) - Prediction method for the deformation of deep foundation pit based on neural network algorithm optimized by particle swarm

✍ Guojin Tan, ; Hanbing Liu, ; Yongchun Cheng, ; Bin Liu, ; Yin Zhang, 📂 Article 📅 2011 🏛 IEEE ⚖ 428 KB