The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting t... more The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models...
The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 a... more The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 and requires urgent global effort to accelerate the development of a vaccine to stop the daily infections and deaths. Several types of vaccine have been designed to teach the immune system how to fight off certain kinds of pathogens. mRNA vaccines are the most important candidate vaccines because of their capacity for rapid development, high potency, safe administration and potential for low-cost manufacture. mRNA vaccine acts by training the body to recognize and response to the proteins produced by disease-causing organisms such as viruses or bacteria. This type of vaccine is the fastest candidate to treat COVID-19 but it currently facing several limitations. In particular, it is a challenge to design stable mRNA molecules because of the inefficient in vivo delivery of mRNA, its tendency for spontaneous degradation and low protein expression levels. This work designed and implemented a ...
International Journal of Advanced Computer Science and Applications, 2017
Feature selection methods for cancer classification are aimed to overcome the high dimensionality... more Feature selection methods for cancer classification are aimed to overcome the high dimensionality of the biomedical data which is a challenging task. Most of the feature selection methods based on DNA methylation are time consuming during testing phase to identify the best pertinent features subset that are relevant to accurate prediction. However, the hybridization between feature selection and extraction methods will bring a method that is far fast than only feature selection method. This paper proposes a framework based on both novel feature selection methods that employ statistical variation, standard deviation and entropy, along with extraction methods to predict cancer using three new features, namely, Hypomethylation, Midmethylation and Hypermethylation. These new features represent the average methylation density of the corresponding three regions. The three features are extracted from the selected features based on the analysis of the methylation behavior. The effectiveness...
DNA methylation plays an important role for initiation and development of human cancers; therefor... more DNA methylation plays an important role for initiation and development of human cancers; therefore, it is used as a biological marker for early detection of cancer. A huge number of features for each sample and a low number of the available samples are two main problems of this field. This paper presents novel vertical, horizontal, and cascaded DNA methylation feature analysis methods in promoter regions. Vertical analysis processes each feature across all normal or cancer samples to get indicators about the methylation level. The generated values are used to select a subset of features within a given threshold. This set undergoes a horizontal analysis process where we group many features into a window that is used to yield a single value. Hence, the original sample size goes through two reduction steps: the first one is an unsupervised feature selection via the vertical analysis of the features and the second one is a feature extraction via the horizontal process of the selected features. For evaluation and comparison, we used traditional feature selection methods and SVD to compare them with the proposed approaches and found that the proposed approaches outperform all other approaches with a good margin. The results of vertical analysis or horizontal analysis alone are better than traditional approaches. Moreover, the results are improved more when combining both types of analysis. With only 97 features, the proposed combined approach is 99.16% accurate while the best traditional classification is only 98.16% accurate with 31 195 features. The combined approach achieved 8.8% to 54.3% improvement percentages compared to all other approaches in terms of a mean absolute error and a root-mean-square error. This indicates that the cascaded approach is far better than the previous approaches. Moreover, the combined approach improves the system accuracy and reduces space and processing complexities of the system.
Due to the vital role of the aberrant DNA methylation during the disease development such as canc... more Due to the vital role of the aberrant DNA methylation during the disease development such as cancer, the comprehension of its mechanism had become essential in the recent years for early detection and diagnosis. With the advent of the high-throughput technologies, there are still several challenges to achieve the classification process using the DNA methylation data. The high-dimensionality and high-noisiness of the DNA methylation data may lead to the degradation of the prediction accuracy. Thus, it becomes increasingly important in a wide range to employ robust computational tools such as feature selection and extraction methods to extract the informative features amongst thousands of them, and hence improving cancer prediction. By using the DNA methylation degree in promoters and probes regions, this paper aims at predicting cancer with a hybridized approach based on the feature selection and feature extraction techniques. The suggested approach exploits a filter feature selection method called (F-score) to overcome the high-dimensionality problem of the DNA methylation data, and proposes an extraction model which employs the peaks of the mean methylation density, the fast Fourier transform algorithm, and the symmetry between the methylation density of a sample and the mean methylation density of both sample types normal and cancer as novel feature extraction methods, in order to accurate cancer classification and reduce training time. To evaluate the reliability of our approach, The naïve base, random forest, and support vector machine algorithms are introduced to predict different cancer types like: breast, colon, head, kidney, lung, thyroid, and uterine with and without the hybridized approach. The results show that, the classification accuracy improves in all most cases and it also proves the reliability indirectly.
The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting t... more The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models...
The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 a... more The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 and requires urgent global effort to accelerate the development of a vaccine to stop the daily infections and deaths. Several types of vaccine have been designed to teach the immune system how to fight off certain kinds of pathogens. mRNA vaccines are the most important candidate vaccines because of their capacity for rapid development, high potency, safe administration and potential for low-cost manufacture. mRNA vaccine acts by training the body to recognize and response to the proteins produced by disease-causing organisms such as viruses or bacteria. This type of vaccine is the fastest candidate to treat COVID-19 but it currently facing several limitations. In particular, it is a challenge to design stable mRNA molecules because of the inefficient in vivo delivery of mRNA, its tendency for spontaneous degradation and low protein expression levels. This work designed and implemented a ...
International Journal of Advanced Computer Science and Applications, 2017
Feature selection methods for cancer classification are aimed to overcome the high dimensionality... more Feature selection methods for cancer classification are aimed to overcome the high dimensionality of the biomedical data which is a challenging task. Most of the feature selection methods based on DNA methylation are time consuming during testing phase to identify the best pertinent features subset that are relevant to accurate prediction. However, the hybridization between feature selection and extraction methods will bring a method that is far fast than only feature selection method. This paper proposes a framework based on both novel feature selection methods that employ statistical variation, standard deviation and entropy, along with extraction methods to predict cancer using three new features, namely, Hypomethylation, Midmethylation and Hypermethylation. These new features represent the average methylation density of the corresponding three regions. The three features are extracted from the selected features based on the analysis of the methylation behavior. The effectiveness...
DNA methylation plays an important role for initiation and development of human cancers; therefor... more DNA methylation plays an important role for initiation and development of human cancers; therefore, it is used as a biological marker for early detection of cancer. A huge number of features for each sample and a low number of the available samples are two main problems of this field. This paper presents novel vertical, horizontal, and cascaded DNA methylation feature analysis methods in promoter regions. Vertical analysis processes each feature across all normal or cancer samples to get indicators about the methylation level. The generated values are used to select a subset of features within a given threshold. This set undergoes a horizontal analysis process where we group many features into a window that is used to yield a single value. Hence, the original sample size goes through two reduction steps: the first one is an unsupervised feature selection via the vertical analysis of the features and the second one is a feature extraction via the horizontal process of the selected features. For evaluation and comparison, we used traditional feature selection methods and SVD to compare them with the proposed approaches and found that the proposed approaches outperform all other approaches with a good margin. The results of vertical analysis or horizontal analysis alone are better than traditional approaches. Moreover, the results are improved more when combining both types of analysis. With only 97 features, the proposed combined approach is 99.16% accurate while the best traditional classification is only 98.16% accurate with 31 195 features. The combined approach achieved 8.8% to 54.3% improvement percentages compared to all other approaches in terms of a mean absolute error and a root-mean-square error. This indicates that the cascaded approach is far better than the previous approaches. Moreover, the combined approach improves the system accuracy and reduces space and processing complexities of the system.
Due to the vital role of the aberrant DNA methylation during the disease development such as canc... more Due to the vital role of the aberrant DNA methylation during the disease development such as cancer, the comprehension of its mechanism had become essential in the recent years for early detection and diagnosis. With the advent of the high-throughput technologies, there are still several challenges to achieve the classification process using the DNA methylation data. The high-dimensionality and high-noisiness of the DNA methylation data may lead to the degradation of the prediction accuracy. Thus, it becomes increasingly important in a wide range to employ robust computational tools such as feature selection and extraction methods to extract the informative features amongst thousands of them, and hence improving cancer prediction. By using the DNA methylation degree in promoters and probes regions, this paper aims at predicting cancer with a hybridized approach based on the feature selection and feature extraction techniques. The suggested approach exploits a filter feature selection method called (F-score) to overcome the high-dimensionality problem of the DNA methylation data, and proposes an extraction model which employs the peaks of the mean methylation density, the fast Fourier transform algorithm, and the symmetry between the methylation density of a sample and the mean methylation density of both sample types normal and cancer as novel feature extraction methods, in order to accurate cancer classification and reduce training time. To evaluate the reliability of our approach, The naïve base, random forest, and support vector machine algorithms are introduced to predict different cancer types like: breast, colon, head, kidney, lung, thyroid, and uterine with and without the hybridized approach. The results show that, the classification accuracy improves in all most cases and it also proves the reliability indirectly.
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Papers by Abeer Raweh