Experiments in Predicting Biodegradability

Džeroski, Sašo; Blockeel, Hendrik; Kompare, Boris; Krämer, Stefan; Pfahringer, Bernhard; Laer, Wim Van

doi:10.1007/3-540-48751-4_9

Cited by 36 publications

(27 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Biodegradability dataset has already been used in the literature for both regression and classification tasks [5]. It consists of 328 structural chemical molecules described in terms of atom and bond.…”

Section: Results On Biodegradabilitymentioning

confidence: 99%

See 1 more Smart Citation

Mr-SBC: A Multi-relational Naïve Bayes Classifier

Ceci

Appice

Malerba

2003

Knowledge Discovery in Databases: PKDD 2003

View full text Add to dashboard Cite

Abstract. In this paper we propose an extension of the naïve Bayes classification method to the multi-relational setting. In this setting, training data are stored in several tables related by foreign key constraints and each example is represented by a set of related tuples rather than a single row as in the classical data mining setting. This work is characterized by three aspects. First, an integrated approach in the computation of the posterior probabilities for each class that make use of first order classification rules. Second, the applicability to both discrete and continuous attributes by means a supervised discretization. Third, the consideration of knowledge on the data model embedded in the database schema during the generation of classification rules. The proposed method has been implemented in the new system Mr-SBC, which is tightly integrated with a relational DBMS. Testing has been performed on two datasets and four benchmark tasks. Results on predictive accuracy and efficiency are in favour of Mr-SBC for the most complex tasks.

show abstract

Section: Results On Biodegradabilitymentioning

confidence: 99%

“…We use a discretized version in order to apply classification systems to the problem. As in [5], four classes have been defined: chemicals degrade fast, moderately, slowly or are resistant. Table 3.…”

Section: Results On Biodegradabilitymentioning

confidence: 99%

Mr-SBC: A Multi-relational Naïve Bayes Classifier

Ceci

Appice

Malerba

2003

Knowledge Discovery in Databases: PKDD 2003

View full text Add to dashboard Cite

show abstract

“…The tables prove that the performance of our models is comparable to the other best results, but not better: GNNs achieve the second best performance on classification task accuracy and on regression task. The high results obtained by methods that do not exploit R0 suggests that the counts of functional groups and substructures are probably a good propositionalization of the relational data (graph connectivity), as already mentioned in Dzeroski et al (1999). On the other hand, the relatively high performance obtained by the presented approaches using only R0 suggest that those methods can actually extract a large part of the information in P0, P1, P2, R1 directly from the original graph.…”

Section: The Biodegradability Datasetmentioning

confidence: 59%

“…In this section, the experimentation carried out on the biodegradability dataset (Dzeroski 1999;Dzeroski et al 1999) is presented. Since the experimental procedure is very similar to that adopted for mutagenesis, in the following we only discuss the differences.…”

Section: The Biodegradability Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Neural networks for relational learning: an experimental comparison

et al. 2010

Self Cite

View full text Add to dashboard Cite

In the last decade, connectionist models have been proposed that can process structured information directly. These methods, which are based on the use of graphs for the representation of the data and the relationships within the data, are particularly suitable for handling relational learning tasks. In this paper, two recently proposed architectures of this kind, i.e. Graph Neural Networks (GNNs) and Relational Neural Networks (RelNNs), are compared and discussed, along with their corresponding learning schemes. The goal is to evaluate the performance of these methods on benchmarks that are commonly used by the relational learning community. Moreover, we also aim at reporting differences in the behavior of the two models, in order to gain insights on possible extensions of the approaches. Since RelNNs have been developed with the specific task of learning aggregate functions in mind, some experiments are run considering that particular task. In addition, we carry out more general experiments on the mutagenesis and the biodegradability datasets, on which several other relational learners have been evaluated. The experimental results are promising and suggest that RelNNs and GNNs can be a viable approach for learning on relational data.

show abstract

SMIREP: Predicting Chemical Activity from SMILES.

Karwath

Raedt

2007

ChemInform

View full text Add to dashboard Cite

Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the first step, a typically large set of fingerprints, or fragments of interest, is constructed (either by hand or by some recent data mining techniques). In the second step, machine learning techniques are applied to obtain a predictive model. The result is often not only a highly accurate but also hard to interpret model. In this paper, we demonstrate the capabilities of a novel SAR algorithm, SMIREP, which tightly integrates the fragment and model generation steps and which yields simple models in the form of a small set of IF-THEN rules. These rules contain SMILES fragments, which are easy to understand to the computational chemist. SMIREP combines ideas from the well-known IREP rule learner with a novel fragmentation algorithm for SMILES strings. SMIREP has been evaluated on three problems: the prediction of binding activities for the estrogen receptor (Environmental Protection Agency's (EPA's) Distributed StructureSearchable Toxicity (DSSTox) National Center for Toxicological Research estrogen receptor (NCTRER) Database), the prediction of mutagenicity using the carcinogenic potency database (CPDB), and the prediction of biodegradability on a subset of the EnVironmental Fate Database (EFDB). In these applications, SMIREP has the advantage of producing easily interpretable rules while having predictive accuracies that are comparable to those of alternative state-of-the-art techniques.

show abstract

Experiments in Predicting Biodegradability

Cited by 36 publications

References 11 publications

Mr-SBC: A Multi-relational Naïve Bayes Classifier

Mr-SBC: A Multi-relational Naïve Bayes Classifier

Neural networks for relational learning: an experimental comparison

SMIREP: Predicting Chemical Activity from SMILES.

Contact Info

Product

Resources

About