Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

Cruz‐Cano, Raul; Chew, David S. H.; Choi, Kwok-Pui; Leung, Ming-Ying

doi:10.1287/ijoc.1090.0360

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Both SVM and ANN suffer from similar woes. The top 250 predictions are selected for SVM and ANN in the same fashion as described in [28] and [19]. The comparison of the performance of the different systems is shown in Table 9.…”

Section: Resultsmentioning

confidence: 99%

Logic minimization and rule extraction for identification of functional sites in molecular sequences

2012

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BackgroundLogic minimization is the application of algebraic axioms to a binary dataset with the purpose of reducing the number of digital variables and/or rules needed to express it. Although logic minimization techniques have been applied to bioinformatics datasets before, they have not been used in classification and rule discovery problems. In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sites in proteins. TFBS are important in various developmental processes and glycosylation is a posttranslational modification critical to protein functions.MethodsIn the present study, we first transformed the original biological dataset into a suitable binary form. Logic minimization was then applied to generate sets of simple rules to describe the transformed dataset. These rules were used to predict TFBS and O-glycosylation sites. The TFBS dataset is obtained from the TRANSFAC database, while the glycosylation dataset was compiled using information from OGLYCBASE and the Swiss-Prot Database.We performed the same predictions using two standard classification techniques, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and used their sensitivities and positive predictive values as benchmarks for the performance of our proposed algorithm. SVM were also used to reduce the number of variables included in the logic minimization approach.ResultsFor both TFBS and O-glycosylation sites, the prediction performance of the proposed logic minimization method was generally comparable and, in some cases, superior to the standard ANN and SVM classification methods with the advantage of providing intelligible rules to describe the datasets. In TFBS prediction, logic minimization produced a very small set of simple rules. In glycosylation site prediction, the rules produced were also interpretable and the most popular rules generated appeared to correlate well with recently reported hydrophilic/hydrophobic enhancement values of amino acids around possible O-glycosylation sites. Experiments with Self-Organizing Neural Networks corroborate the practical worth of the logic minimization method for these case studies.ConclusionsThe proposed logic minimization algorithm provides sets of rules that can be used to predict TFBS and O-glycosylation sites with sensitivity and positive predictive value comparable to those from ANN and SVM. Moreover, the logic minimization method has the additional capability of generating interpretable rules that allow biological scientists to correlate the predictions with other experimental results and to form new hypotheses for further investigation. Additional experiments with alternative rule-extraction techniques demonstrate that the logic minimization method is able to produce accurate rules from datasets with large numbers of variables and limited numbers of positive examples.

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Section: Resultsmentioning

confidence: 99%

Logic minimization and rule extraction for identification of functional sites in molecular sequences

2012

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“…Our approach might also be adapted to other loss functions, such as the so-called ramp loss, [9], by replacing (2) with the corresponding SVM problem. The same happens if the SVM in (2) is replaced by some related methods such as the least-squares SVM, e.g., [21].…”

Section: Conclusion and Extensionsmentioning

confidence: 96%

Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm

Blanquero

Carrizosa

Jiménez-Cordero

et al. 2019

European Journal of Operational Research

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Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Machine (SVM) is a benchmark tool for classification, in particular, of functional data. SVM is frequently used with a kernel (e.g.: Gaussian) which involves a scalar bandwidth parameter. In this paper, we propose to use kernels with functional bandwidths. In this way, accuracy may be improved, and the time intervals critical for classification are identified. Tuning the functional parameters of the new kernel is a challenging task expressed as a continuous optimization problem, solved by means of a heuristic. Our experiments with benchmark data sets show the advantages of using functional parameters and the effectiveness of our approach.

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“…Ensembled with previously proposed methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Recursive feature elimination was used to extract the most informative attri-butes and provides important domain knowledge in terms of the most significant features of the data sets [95] further conclude LS-SVMs can potentially be a very reliable and robust tool for viral replication origin prediction.…”

Section: Miscellaneous Examplesmentioning

confidence: 99%

Application of Support Vector Machines in Viral Biology

Modak

Mehta

Sehgal

et al. 2019

Global Virology III: Virology in the 21st Century

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Novel experimental and sequencing techniques have led to an exponential explosion and spiraling of data in viral genomics. To analyse such data, rapidly gain information, and transform this information to knowledge, interdisciplinary approaches involving several different types of expertise are necessary. Machine learning has been in the forefront of providing models with increasing accuracy due to development of newer paradigms with strong fundamental bases. Support Vector Machines (SVM) is one such robust tool, based rigorously on statistical learning theory. SVM provides very high quality and robust solutions to classification and regression problems. Several studies in virology employ high performance tools including SVM for identification of potentially important gene and protein functions. This is mainly due to the highly beneficial aspects of SVM. In this chapter we briefly provide lucid and easy to understand details of SVM algorithms along with applications in virology.

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Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

Cited by 8 publications

References 46 publications

Logic minimization and rule extraction for identification of functional sites in molecular sequences

Logic minimization and rule extraction for identification of functional sites in molecular sequences

Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm

Application of Support Vector Machines in Viral Biology

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