A Comparative Study of Machine Learning Methods for Detecting Promoters in Bacterial DNA Sequences

Abstract

Abstract. Machine Learning methods have been widely used in bioinformatics, mainly for data classification and pattern recognition. The detection of genes in DNA sequences is still an open problem. Identifying the promoter region laying prior the gene itself is an important aid to detect a gene. This paper aims at applying several Machine Learning methods to the construction of classifiers for detection of promoters in the DNA of Escherichia coli. A thorough comparison of methods was done. In general, probabilistic and neural network-based methods were those that performed better regarding accuracy rate.

A Two-Stage Evolutionary Approach for Effective Classification of Hypersensitive Dna Sequences

“…The classifiers chosen are based on previous research in sequence classifications which had yielded better results. 38 Table 5 summarizes the comparative analysis of our approach with various traditional classification systems. The Appendix defines the parameters for each of the methods.…”

Evaluation of Weight Matrix Models in the splice junction recognition problem

“…The direct comparison of results obtained here with other classifiers for the same data sets (such as [21]) may lead to interpretation errors, since different partitions of the data sets and cross-validation procedures can be used. However, as shown in [19] probabilistic-like methods, such as WWAM and MDD, tend to achieve better predictive accuracy than other methods for biological sequences.…”

“…Such a measure, used for two-class problems, is the Matthews Correlation Coefficient (MCC) (Eq. 4), regarded as a balanced measure and frequently used in bioinformatics [1], [19].…”

A Comparative Study of Machine Learning and Evolutionary Computation Approaches for Protein Secondary Structure Classification

“…The ROC plot is a useful technique for visualizing and comparing classifiers and is commonly used in decision making in ML, data mining and Bioinformatics (Sing et al, 2005;Tavares et al, 2008). It is constructed using the performance rate of the classifiers.…”

“…HMMs offer the advantages of having strong statistical foundations that are well-suited to natural language domains and they are computationally efficient (Seymore et al, 1999). Therefore, HMMs have been used for pattern recognition in many domains and, in special, in Bioinformatics (Durbin et al, 1998;Tavares et al, 2008).…”