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Learning patterns in the dynamics of biological networks
 In KDD
, 2009
"... Our dynamic graphbased relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic networks is important not only to understand life at the systemlevel, but also to discover novel patterns in other structural d ..."
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Cited by 12 (0 self)
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Our dynamic graphbased relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic networks is important not only to understand life at the systemlevel, but also to discover novel patterns in other structural data. Most current graphbased data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence. These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph. Then, our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks. The discovered graphrewriting rules show how biological networks change over time, and the transformation rules show the repeated patterns in the structural changes. In this paper, we apply our approach to biological networks to evaluate our approach and to understand how the biosystems change over time. We evaluate our results using coverage and prediction metrics, and compare to biological literature.
Graphbased data mining in dynamic networks: Empirical comparison of compressionbased and frequencybased subgraph mining
 IN ICDM WORKSHOP ON ADN
, 2008
"... We propose a dynamic graphbased relational mining approach using graphrewriting rules to learns patterns in networks that structurally change over time. A dynamic graph containing a sequence of graphs over time represents dynamic properties as well as structural properties of the network. Our appr ..."
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Cited by 6 (3 self)
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We propose a dynamic graphbased relational mining approach using graphrewriting rules to learns patterns in networks that structurally change over time. A dynamic graph containing a sequence of graphs over time represents dynamic properties as well as structural properties of the network. Our approach discovers graphrewriting rules, which describe the structural transformations between two sequential graphs over time, and also learns description rules that generalize over the discovered graphrewriting rules. The discovered graphrewriting rules show how networks change over time, and the description rules in the graphrewriting rules show temporal patterns in the structural changes. We apply our approach to biological networks to understand how the biosystems change over time. Our compressionbased discovery of the description rules is compared with the frequent subgraph mining approach using several evaluation metrics.
A survey of graph mining techniques for biological datasets, Managing and Mining Graph Data
, 2010
"... ..."
Temporal and Structural Analysis of Biological Networks
"... Our project introduces a graphbased relational learning approach using graphrewriting rules for temporal and structural analysis of biological networks changing over time. The analysis of dynamic biological networks is necessary to understand life at the systemlevel, because biological networks c ..."
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Cited by 2 (1 self)
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Our project introduces a graphbased relational learning approach using graphrewriting rules for temporal and structural analysis of biological networks changing over time. The analysis of dynamic biological networks is necessary to understand life at the systemlevel, because biological networks continuously change their structures and properties while an organism performs various biological activities to promote reproduction and sustain our lives. Most current graphbased data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Most approaches for analysis of microarray data disregard structural properties on biological systems. But our dynamic graphbased relational learning approach describes how the graphs temporally and structurally change over time in the dynamic graph representing biological networks in combination with microarray data. 1
Dynamic Graphbased Relational Learning of Temporal Patterns in Biological Networks Changing over Time
"... We propose a dynamic graphbased relational learning approach using graphrewriting rules to analyze how biological networks change over time. The analysis of dynamic biological networks is necessary to understand life at the systemlevel, because biological networks continuously change their structu ..."
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We propose a dynamic graphbased relational learning approach using graphrewriting rules to analyze how biological networks change over time. The analysis of dynamic biological networks is necessary to understand life at the systemlevel, because biological networks continuously change their structures and properties while an organism performs various biological activities to promote reproduction and sustain our lives. Most current graphbased data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. First, we generate a dynamic graph, which is a sequence of graphs representing biological networks changing over time. Then, our approach discovers graphrewriting rules, which show how to replace subgraphs, between two sequential graphs. These rewriting rules describe the structural difference between two graphs, and describe how the graphs in the dynamic graph change over time. Temporal relational patterns discovered in dynamic graphs representing synthetic networks and metabolic pathways show that our approach enables the discovery of dynamic patterns in biological networks.
High Confidence Predictions of DrugDrug Interactions: Predicting Affinities for Cytochrome P450 2C9 with Multiple Computational Methods
, 2007
"... Four different models are used to predict whether a compound will bind to 2C9 with a Ki value of less than 10 µM. A training set of 276 compounds and a diverse validation set of 50 compounds were used to build and assess each model. The modeling methods are chosen to exploit the differences in how t ..."
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Four different models are used to predict whether a compound will bind to 2C9 with a Ki value of less than 10 µM. A training set of 276 compounds and a diverse validation set of 50 compounds were used to build and assess each model. The modeling methods are chosen to exploit the differences in how training sets are used to develop the predictive models. Two of the four methods develop partitioning trees based on global descriptions of structure using nine descriptors. A third method uses the same descriptors to develop local descriptions that relate activity to structures with similar descriptor characteristics. The fourth method uses a graphtheoretic approach to predict activity based on molecular structure. When all of these methods agree, the predictive accuracy is 94%. An external validation set of 11 compounds gives a predictive accuracy of 91 % when all methods agree.