Evaluating deterministic motif significance measures in protein databases

<p>Abstract</p> <p>Background</p> <p>Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.</p> <p>Results</p> <p>From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs.</p> <p>Conclusion</p> <p>In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.</p

GOPred: GO Molecular Function Prediction by Combined Classifiers

Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred)

GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima

BACKGROUND: Computational discovery of transcription factor binding sites (TFBS) is a challenging but important problem of bioinformatics. In this study, improvement of a Gibbs sampling based technique for TFBS discovery is attempted through an approach that is widely known, but which has never been investigated before: reduction of the effect of local optima. RESULTS: To alleviate the vulnerability of Gibbs sampling to local optima trapping, we propose to combine a thermodynamic method, called simulated tempering, with Gibbs sampling. The resultant algorithm, GibbsST, is then validated using synthetic data and actual promoter sequences extracted from Saccharomyces cerevisiae. It is noteworthy that the marked improvement of the efficiency presented in this paper is attributable solely to the improvement of the search method. CONCLUSION: Simulated tempering is a powerful solution for local optima problems found in pattern discovery. Extended application of simulated tempering for various bioinformatic problems is promising as a robust solution against local optima problems

Application Of The Fuzzy Min-Max Neural Network Classifier To Problems With Continuous And Discrete Attributes

. The fuzzy min-max classification network constitutes a promisimg pattern recognition approach that is based on hyberbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The definition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max network to a problem with continous and discrete attributes, requires the modification of its definition and operation in order to deal with the discrete dimensions. Experimental results using the modified model on a difficult pattern recognition problem establishes the strengths and weaknesses of the proposed approach. INTRODUCTION Fuzzy min-max neural networks [2, 3] consitute one of the many models of computational intelligence that have been recently developed from research efforts aiming at synthesizing neural networks and fuzzy logic [1]. The fuzzy min-max classification neural network [2] is an on-line supervised learning cl..

Autonomous vehicle navigation using evolutionary reinforcement learning

Reinforcement learning schemes perform direct on-line search in control space. This makes them appropriate for modifying control rules to obtain improvements in the performance of a system. The effectiveness of a reinforcement learning strategy is studied here through the training of a learning classz$er system (LCS) that controls the movement of an autonomous vehicle in simulated paths including left and right turns. The LCS comprises a set of condition-action rules (classifiers) that compete to control the system and evolve by means of a genetic algorithm (GA). Evo-lution and operation of classifiers depend upon an appropriate credit assignment mechanism based on reinforcement learning. Different design options and the role of various parameters have been investigated experimentally. The per-formance of vehicle movement under the proposed evolutionary approach is superior compared with that of other (neural) approaches based on reinforcement learning that have been applied previously to the same benchmark prob-lem. 0 1998 Elsevier Science B.V

Application of the Fuzzy Min-Max Neural Network Classifier to Problems with Continuous and Discrete Attributes

. The fuzzy min-max classification network constitutes a promisimg pattern recognition approach that is based on hyberbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The definition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max network to a problem with continous and discrete attributes, requires the modification of its definition and operation in order to deal with the discrete dimensions. Experimental results using the modified model on a difficult pattern recognition problem establishes the strengths and weaknesses of the proposed approach. INTRODUCTION Fuzzy min-max neural networks [2, 3] consitute one of the many models of computational intelligence that have been recently developed from research efforts aiming at synthesizing neural networks and fuzzy logic [1]. The fuzzy min-max classification neural network [2] is an on-line supervised learning cl..