Correlation between different DNA period-3 signals: An analytical study for exons prediction

El-Badawy, Ismail M.; Aziz, Ashraf M.; Omar, Zaid; Malarvili, M. B.

doi:10.1109/apsipa.2017.8282195

Cited by 5 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consider two variables X and Y (here X and Y are the magnitude spectra M i and M j of two signals), each of length p , that is, X ={ X 0 ,…, X p −1 } and Y ={ Y 0 ,…, Y p −1 }. The Pearson Correlation Coefficient r XY between X and Y is the ratio of their covariance (measure of how much X and Y vary together) to the product of their standard deviations [52, 53], that is, …”

Section: Methods and Implementationmentioning

confidence: 99%

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

2019

View full text Add to dashboard Cite

Background Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results We propose a novel combination of supervised M achine L earning with D igital S ignal P rocessing, resulting in ML-DSP : an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97 % . A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. Conclusions Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.

show abstract

Section: Methods and Implementationmentioning

confidence: 99%

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

2019

View full text Add to dashboard Cite

show abstract

“…In this work, we made use of the Pearson correlation coefficient, receiver operating characteristic (ROC) curve and p-value to evaluate the relevance of the extracted features [40]- [46]. The Pearson correlation coefficient 'ρ', which indicates the linear relationship between a feature F j = [f j1 , f j2 , ..., f jM ] and target class labels L = [l 1 , l 2 , ..., l M ], is defined as their covariance divided by the square root of the product of their variances as given below [40], [41], [47]:…”

Section: Features Evaluation and Selectionmentioning

confidence: 99%

“…and M represents the number of capnogram segments that are used in our experiment (i.e., M = 200). The range of the Pearson correlation coefficient is as follows [40], [41]:…”

Section: Features Evaluation and Selectionmentioning

confidence: 99%

An Effective Machine Learning Approach for Classifying Artefact-Free and Distorted Capnogram Segments Using Simple Time-Domain Features

2022

Self Cite

View full text Add to dashboard Cite

Capnogram signal analysis has received considerable attention owing to its important applications in assessing cardiopulmonary functions. However, the automatic elimination of deformed parts of a capnogram waveform remains an open research problem. Herein, we introduce an automatic classification approach for discriminating artefact-free (regular) and distorted (irregular) segments of capnogram signals. The proposed features include Hjorth parameters and mean absolute deviation (MAD). The main advantage of these features is their simplicity, such that they can be employed in a computationally efficient machine learning algorithm. MATLAB simulation is conducted on 100 regular and 100 irregular segments of capnogram to extract the proposed and existing features, which are ranked based on the Pearson correlation coefficient, p-value and area under receiver operating characteristic (ROC) curve. The naive Bayes, decision tree, random forest and support vector machine (SVM) classifiers are fed by the relatively highly ranked features, and the classification performance is assessed via ten-fold cross-validation. Besides the linear kernel SVM, the radial basis function (RBF) and polynomial kernel functions with different orders are also included in the current experiment. Results revealed the effectiveness of the Hjorth activity and MAD attributes when used with the fourth-order polynomial kernel-SVM classifier. The achieved accuracy, precision and specificity are 89%, 92.1%, and 91% outperforming the existing method by 2.5%, 5.6% and 7%, respectively. The simplicity of the proposed time-domain features is confirmed by the average total computational time of features extraction and classification phases which is only 13 ms instead of 19 ms in the case of incorporating both time-and frequency-domain features, indicating a reduction of 31.6%. It is envisaged that the proposed approach can be valuable if implemented with capnography devices for real-time and fully automated capnogram-based respiratory assessment. Even so, further research is recommended to enhance the classification performance through exploring more features and/or classifiers.INDEX TERMS Hjorth parameters, mean absolute deviation, capnography, support vector machine, pulmonary diseases, carbon dioxide, clinical monitoring.

show abstract

“…, Y p−1 }. The Pearson correlation coefficient r XY between X and Y is the ratio of their covariance (measure of how much X and Y vary together) to the product of their standard deviations [52,53], that is,…”

Section: Pearson Correlation Coefficient (Pcc)mentioning

confidence: 99%

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

Randhawa

Hill

Kari

2018

Preprint

View full text Add to dashboard Cite

Background: Although methods and software tools abound for the comparison, analysis, identification, and taxonomic classification of the enormous amount of genomic sequences that are continuously being produced, taxonomic classification remains challenging. The difficulty lies within both the magnitude of the dataset and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results: We combine supervised Machine Learning with Digital Signal Processing to design ML-DSP, an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels.We test ML-DSP by classifying 7,396 full mitochondrial genomes from the kingdom to genus levels, with 98% classification accuracy. Compared with the alignment-based classification tool MEGA7 (with sequences aligned with either MUSCLE, or CLUSTALW), ML-DSP has similar accuracy scores while being significantly faster on two small benchmark datasets (2,250 to 67,600 times faster for 41 mammalian mitochondrial genomes). ML-DSP also successfully scales to accurately classify a large dataset of 4,322 complete vertebrate mtDNA genomes, a task which MEGA7 with MUSCLE or CLUSTALW did not complete after several hours, and had to be terminated. ML-DSP also outperforms the alignment-free tool FFP (Feature Frequency Profiles) in terms of both accuracy and time, being three times faster for the vertebrate mtDNA genomes dataset. Conclusions: We provide empirical evidence that ML-DSP distinguishes complete genome sequences at all taxonomic levels. Ultrafast and accurate taxonomic classification of genomic sequences is predicted to be highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures, in identifying mechanistic determinants of genomic signatures, and in evaluating genome integrity.

show abstract

Correlation between different DNA period-3 signals: An analytical study for exons prediction

Cited by 5 publications

References 15 publications

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

An Effective Machine Learning Approach for Classifying Artefact-Free and Distorted Capnogram Segments Using Simple Time-Domain Features

ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels

Contact Info

Product

Resources

About