Graph networks for molecular design

Graph Memory Networks for Molecular Activity Prediction

Molecular activity prediction is critical in drug design. Machine learning techniques such as kernel methods and random forests have been successful for this task. These models require fixed-size feature vectors as input while the molecules are variable in size and structure. As a result, fixed-size fingerprint representation is poor in handling substructures for large molecules. Here we approa...

Graph Theory for Survivability Design in Communication Networks

H∞ Sampled-Data Controller Design for Stochastic Genetic Regulatory Networks

Artificially regulating gene expression is an important step in developing new treatment for system-level disease such as cancer. In this paper, we propose a method to regulate gene expression based on sampled-data measurements of gene products concentrations. Inherent noisy behaviour of Gene regulatory networks are modeled with stochastic nonlinear differential equation. To synthesize feed...

A Novel Approach for Detecting Relationships in Social Networks Using Cellular Automata Based Graph Coloring

All the social networks can be modeled as a graph, where each roles as vertex and each relationroles as an edge. The graph can be show as G = [V;E], where V is the set of vertices and E is theset of edges. All social networks can be segmented to K groups, where there are members in eachgroup with same features. In each group each person knows other individuals and is in touch ...

A Multi Objective Graph Based Model for Analyzing Survivability of Vulnerable Networks

In the various fields of disaster management, choosing the best location for the Emergency Support & Supply Service Centers (ESSSCs) and the survivability of the network that provides the links between ESSSCs and their environment has a great role to be paid enough attention. This paper introduces a graph based model to measure the survivability of the linking's network. By values computed for ...