The Predictive Toxicology Challenge 2000–2001

Helma, Christoph; King, Ross D.; Krämer, Stefan; Srinivasan, Ashwin

doi:10.1093/bioinformatics/17.1.107

Cited by 185 publications

(79 citation statements)

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“…For the Demospongiae data set, we report results both using the complete data set as well as using a subset consisting of 280 Demospongiae and 3 solution classes. Finally, the Toxicology data set (PTC) is a highly relational data set introduced as a challenge in the ECML/PKDD 2001 conference (Helma et al 2001). We have selected PTC to evaluate the scalability of our similarity measures rather than their accuracy; thus, we present results for Toxicology in a separate section.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Similarity measures over refinement graphs

Ontañón

Plaza

2011

Mach Learn

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Similarity also plays a crucial role in support vector machines. Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will define and analyze two similarity measures, S λ and S π , based on refinement graphs. The anti-unification-based similarity, S λ , assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The property-based similarity, S π , is based on a process of disintegrating the instances into a set of properties, and then analyzing these property sets. Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness.

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Section: Experimental Evaluationmentioning

confidence: 99%

Similarity measures over refinement graphs

Ontañón

Plaza

2011

Mach Learn

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“…If the upper bound calculated by Equation 9 is lower than the required support threshold minSup, i.e., esup(S , D P ) < minSup (10) then the subgraph pattern S can be safely pruned. Notice that this pruning process of candidate patterns is very efficient for two reasons.…”

Section: Computing Expected Supportmentioning

confidence: 99%

Efficient discovery of frequent subgraph patterns in uncertain graph databases

Papapetrou

Ioannou

Skoutas

2011

Proceedings of the 14th International Conference on Extending Database Technology

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Mining frequent subgraph patterns in graph databases is a challenging and important problem with applications in several domains. Recently, there is a growing interest in generalizing the problem to uncertain graphs, which can model the inherent uncertainty in the data of many applications. The main difficulty in solving this problem results from the large number of candidate subgraph patterns to be examined and the large number of subgraph isomorphism tests required to find the graphs that contain a given pattern. The latter becomes even more challenging, when dealing with uncertain graphs. In this paper, we propose a method that uses an index of the uncertain graph database to reduce the number of comparisons needed to find frequent subgraph patterns. The proposed algorithm relies on the apriori property for enumerating candidate subgraph patterns efficiently. Then, the index is used to reduce the number of comparisons required for computing the expected support of each candidate pattern. It also enables additional optimizations with respect to scheduling and early termination, that further increase the efficiency of the method. The evaluation of our approach on three real-world datasets as well as on synthetic uncertain graph databases demonstrates the significant cost savings with respect to the state-of-the-art approach.

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“…For experiments with real world data, we selected the Mutagenesis data set introduced by [17] and the Predictive Toxicology Challenge (PTC) data introduced by set [18]. The Mutagenesis data set has been used as a benchmark data set in graph classification for many years.…”

Section: A Datasets and Representationmentioning

confidence: 99%

Empirical comparison of graph classification algorithms

Ketkar

Holder

Cook

2009

2009 IEEE Symposium on Computational Intelligence and Data Mining

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Abstract-The graph classification problem is learning to classify separate, individual graphs in a graph database into two or more categories. A number of algorithms have been introduced for the graph classification problem. We present an empirical comparison of the major approaches for graph classification introduced in literature, namely, SubdueCL, frequent subgraph mining in conjunction with SVMs, walkbased graph kernel, frequent subgraph mining in conjunction with AdaBoost and DT-CLGBI. Experiments are performed on five real world data sets from the Mutagenesis and Predictive Toxicology domain which are considered benchmark data sets for the graph classification problem. Additionally, experiments are performed on a corpus of artificial data sets constructed to investigate the performance of the algorithms across a variety of parameters of interest.Our conclusions are as follows. In datasets where the underlying concept has a high average degree, walk-based graph kernels perform poorly as compared to other approaches. The hypothesis space of the kernel is walks and it is insufficient at capturing concepts involving significant structure. In datasets where the underlying concept is disconnected, SubdueCL performs poorly as compared to other approaches. The hypothesis space of SubdueCL is connected graphs and it is insufficient at capturing concepts which consist of a disconnected graph. FSG+SVM, FSG+AdaBoost, DT-CLGBI have comparable performance in most cases.

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The Predictive Toxicology Challenge 2000–2001

Cited by 185 publications

References 1 publication

Similarity measures over refinement graphs

Similarity measures over refinement graphs

Efficient discovery of frequent subgraph patterns in uncertain graph databases

Empirical comparison of graph classification algorithms

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