Papers by Mai Mohamed Said Mabrouk
2010 2nd International Conference on Computer Technology and Development, 2010
A novel in fl uenza A (HINI) virus of swine origin emerged in the spring of 2009 and spread very ... more A novel in fl uenza A (HINI) virus of swine origin emerged in the spring of 2009 and spread very rapidly among people. The severity of the disease and the number of deaths caused by a pandemic virus varies greatly and can change over time. For these reasons it becomes a challenge to study the genomic features that characterize this new virus. At the same time, the theory and methods of signal processing are becoming increasingly important in molecular biology, especially in the analysis of genomic sequences. Throughout this work we have characterized these sequences according to their chaotic features such as moment invariants, correlation dimension, and largest Lyapunov exponent estimates. We have applied our method to a number of sequences encoded into a time series using EIIP sequence indicators. In order to extract genomic features that can distinguish the new swine flu from the classical HtNt existed before using sequences from segment 8 which encodes two important proteins for immune system attack (NSI and NS2). Our results indicate that this study have yielded a significant differences between the two types of influenza HtNt (Pandemic and classical). Keywordspandemic flu; nonlinear dynamics; moment invariantscorrelation dimension; Lya p unov exponent. I.
Measurement of foot pressure distribution is clinically useful because it can identify anatomical... more Measurement of foot pressure distribution is clinically useful because it can identify anatomical foot deformities, guide the diagnosis and treatment of gait disorders and falls, as well leads to strategies for preventing pressure ulcers in diabetes. This study was conducted to investigate the differences in plantar pressure distribution in normal subjects at four points during symmetrical standing position. The peak plantar pressure was measured below four points of each foot (big toe, lateral aspect of the foot, head of first metatarsal and mid heel) in male and female subjects. Results revealed that there were significant difference between the two groups at the level of mid heel, big toe, and head of the first metatarsal, while there was no significant difference at the level of metatarsal heads. Statistical t-test was used to compare plantar pressure distribution between the dominant and non-dominant limbs in each group. The test results indicated that upper limb anthropometry is significantly different between females and males with p-value of 0.025. Mean value of males (25.54) is higher than females (16.99). Left heel pressure is significantly different between females and males with p-value of 0.008. Mean value of females (0.87) is higher than males (0.45). Right heel planter pressure is highly significantly different between females and males with p-value < 0.001. Mean of females (1.35) is higher than males (0.54), whereas all other variables are not significantly different between females and males. Load asymmetries during quiet standing has not received much research attention, they may greatly extend our understanding of the upright stance stability control. It seems that limb load asymmetry factor may serve as a vertical measure of postural stability and thus it can be used for early diagnostics of the age related decline in balance control.
Diagnosis and treatment of several disorders affecting the retina and the choroid behind it requi... more Diagnosis and treatment of several disorders affecting the retina and the choroid behind it require capturing a sequence of fundus images using the fundus camera. These images are to be processed for better diagnosis and planning of treatment. Retinal image segmentation is greatly required to extract certain features that may help in diagnosis and treatment. Also registration of retinal images is very useful in extracting the motion parameters that help in composing a complete map for the retina as well as in retinal tracking. This paper introduces a survey for the segmentation and registration techniques that were reported as being well for retinal images.
Many digital signal processing, techniques have been used to automatically distinguish protein co... more Many digital signal processing, techniques have been used to automatically distinguish protein coding regions (exons) from non-coding regions (introns) in DNA sequences. In this work, we have characterized these sequences according to their nonlinear dynamical features such as moment invariants, correlation dimension, and largest Lyapunov exponent estimates. We have applied our model to a number of real sequences encoded into a time series using EIIP sequence indicators. In order to discriminate between coding and non coding DNA regions, the phase space trajectory was first reconstructed for coding and non-coding regions. Nonlinear dynamical features are extracted from those regions and used to investigate a difference between them. Our results indicate that the nonlinear dynamical characteristics have yielded significant differences between coding (CR) and non-coding regions (NCR) in DNA sequences. Finally, the classifier is tested on real genes where coding and non-coding regions ...
Avicenna Journal of Medical Biotechnology, 2011
Genomic Signal Processing is a relatively new field in bioinformatics, in which signal processing... more Genomic Signal Processing is a relatively new field in bioinformatics, in which signal processing algorithms and methods are used to study functional structures in the DNA. An appropriate mapping of the DNA sequence into one or more numerical sequences enables the use of many digital signal processing tools in the analysis of different genomic sequences. Also, a novel Influenza A (H1N1) virus of swine origin emerged in the spring of 2009 and spread very rapidly among people. The severity of the disease and the number of deaths caused by a pandemic virus varies greatly and can change over time. Throughout this work, Pandemic H1N1 genomic sequences were characterized according to nonlinear dynamical features such as moment invariants and largest Lyapunov exponents and then compared to those features that extracted from classical H1N1 genomic sequences. The proposed methods were applied to a number of sequences encoded into a time series using a coding measure scheme employing Electron-Ion Interaction Pseudopotential (EIIP). The aim of this work is to extract genomic features that can distinguish the new swine flu from the classical H1N1 existed before using sequences from segment 8 of the influenza genome that consists of 8 RNA segments which encodes two important proteins for immune system attack (NS1 and NS2). According to the obtained results it is evident that variability is present based on a significance test in both groups; pandemic and classical H1N1 sequences.
Advances in Computing, 2014
Due to the vast success of bioengineering techniques, a series of large scale analysis tools has ... more Due to the vast success of bioengineering techniques, a series of large scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. The analysis of DNA microarray image consists of several steps; gridding, segmentation, and quantification that can significantly deteriorate the quality of gene expression in formation, and hence decrease our confidence in any derived research results. Thus, microarray data processing steps become critical for performing optimal microarray data analysis and deriving meaningful biological information from microarray images. Gridding; the first processing step in microarray image analysis, is to allocate each spot of the array inside a distinct window. The second step which is highly affected by gridding is segmentation. It is the process, by which each individual cell in the grid must be selected to determine the spot signal and to estimate the background hybridization. In this paper, an accurate and fully automated gridding method is applied to prepare the image for the Segmentation step. For segmenting the microarray image four segmentation methods are explored; "fixed circle", "adaptive circle", "thresholding", and "adaptive shape" segmentation. By comparing the results of segmentation, it was found that the "adaptive shape segmentation method" can segment noisy microarray images correctly, gives high accuracy results and minimal processing time, and can be applied to various types of noisy microarray images.
PLOS ONE, 2015
Rheumatoid arthritis (RA) is an autoimmune disease which has a significant socioeconomic impact. ... more Rheumatoid arthritis (RA) is an autoimmune disease which has a significant socioeconomic impact. The aim of the current study was to investigate eight candidate RA susceptibility loci to identify the associated variants in Egyptian population. Eight single nucleotide polymorphisms (SNPs) (MTHFR-C677T and A1298C, TGFβ1 T869C, TNFB A252G, and VDR-ApaI, BsmI, FokI, and TaqI) were tested by genotyping patients with RA (n = 105) and unrelated controls (n = 80). Associations were tested using multiplicative, dominant, recessive, and co-dominant models. Also, the linkage disequilibrium (LD) between the VDR SNPs was measured to detect any indirect association. By comparing RA patients with controls (TNFB, BsmI, and TaqI), SNPs were associated with RA using all models. MTHFR C677T was associated with RA using all models except the recessive model. TGFβ1 and MTHFR A1298C were associated with RA using the dominant and the co-dominant models. The recessive model represented the association for ApaI variant. There were no significant differences for FokI and the presence of RA disease by the used models examination. For LD results, There was a high D 0 value between BsmI and FokI (D 0 = 0.91), but the r 2 value between them was poor. All the studied SNPs may contribute to the susceptibility of RA disease in Egyptian population except for FokI SNP.
Effect of MTHFR, TGFβ1, and TNFB polymorphisms on osteoporosis in rheumatoid arthritis patients
Gene, 2015
Extraction of prediction rules: Protein secondary structure prediction
2014 10th International Computer Engineering Conference (ICENCO), 2014
Protein structure prediction is one of the most important problems in bioinformatics. Protein'... more Protein structure prediction is one of the most important problems in bioinformatics. Protein's secondary structure prediction is a key step in prediction of three-dimensional structure of protein. Despite all the efforts made, so far finding an accurate computational approach to solving a protein structure problem remains a challenging problem. Many computational techniques have been used to predict protein secondary structure (PSS) however, only few of such researches to have dealt with logic rules fundamental to prediction itself. This study, aims to combine decision trees at output of supporting vector machines (SVM) to extract rules governing protein secondary structure prediction. The rules share remarkable relations between the prediction model and the biological meaning. Moreover, they improved the intelligible of protein secondary structure prediction by providing an inside to the predicting model itself. Results revealed that the proposed rules were generated on RS126 data set, and these rules can also be explained biologically.
Adaptation of cuckoo search algorithm for the Motif Finding problem
2014 10th International Computer Engineering Conference (ICENCO), 2014
In Bioinformatics, Motif Finding is defined as the ability to locate repeated patterns in the seq... more In Bioinformatics, Motif Finding is defined as the ability to locate repeated patterns in the sequence of nucleotides or amino acids. Identifying these motifs in DNA sequences is a computationally hard problem which requires efficient algorithms. Cuckoo search (CS) is a new promising metaheuristic search algorithm , CS has been inspired by the breeding behavior of cuckoos and belongs to a class of novel nature-inspired algorithm. CS has been successfully applied to solve continuous optimization problems; however, its ability to solve discrete problems has not been sufficiently explored. In this paper, applying CS algorithm for solving Planted Motif Problems is proposed. Experimental results show that the proposed adaptation can find the motifs fast and efficiently compared to other existing algorithms.
DNA microarray is an innovative tool for gene studies in biomedical research, and its application... more DNA microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. Image processing is an important aspect of microarray experiments, the primary purpose of the image analysis step is to extract numerical foreground and background intensities for the red and green channels for each spot on the microarray. The background intensities are used to correct the foreground intensities for local variation on the array surface, resulting in corrected red and green intensities for each spot that can be considered as a primary data for subsequent analysis. Most techniques divide the overall microarray image processing into three steps: gridding, segmentation, and quantification. In this paper, a simple automated gridding technique is developed with a great effect on noisy microarray images. A segmentation technique based on "edge-detection" is applied to identify the spots and separate the foreground from the background is known as microarray image segmentation. Finally, a quantification technique is used to calculate the gene expression level from the intensity values of the red and green components of the image. Results revealed that the developed methods can deal with various kinds of noisy microarray images, with high griddingaccuracy of 92.2% for low quality images and 100% for high quality images resulting in better spot quantification to get more accurate gene expression values.
Diabetes and chronic liver disease (CLD) are common long-term conditions in the developed and dev... more Diabetes and chronic liver disease (CLD) are common long-term conditions in the developed and developing world. Patients with liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. The liver has many essential functions, and liver disease presents a number of concerns for the delivery of medical care. Classification techniques are very popular in various automatic medical diagnosis tools. Early identification of the cancer has been often vital for the survival of the patients. Support vector machine (SVM) are supervised learning models with associated learning algorithms that analyze data and recognize patterns. In this work, Support vector machine is used for classifying liver disease using diabetes disease dataset and two liver patents datasets ,evaluating a support vector machine classifier by measuring its performance based on: accuracy, error rate, sensitivity, prevalence and specificity. Results show that the accuracy, error rate and specificity at first 8ordered features are the best for Diabetes diagnosis dataset compared to other two datasets. The sensitivity and prevalence at first 8ordered features are the best for AP Liver dataset compared to other two datasets.
International Journal of Computer Applications, 2013
DNA Microarray is an innovative tool for gene studies in biomedical research, and its application... more DNA Microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. It is capable of testing and extracting the expression of large number of genes in parallel. The gene expression process is divided into three basic steps: gridding, segmentation, and quantification. Automatic gridding; which is to assign coordinates to every element of the spot array, is considered the most challenging phase of microarrays image processing. For processing of microarray images, a new, automatic, fast and accurate approach is proposed for gridding noisy cDNA microarray images. In the real world, microarray image doesn't reflect measures of the fluorescence intensities for the dye of interest only, as different kinds of noise and artifacts can be observed. In this paper, a novel gridding method based on projection is developed accompanied by a pre-processing, post-processing, and refinement steps for noisy microarray images. Results revealed that the proposed method is used with high accuracy and minimal processing time and can be applied to various types of noisy microarray images.
Journal of Biomedical Engineering and Medical Imaging, 2014
Due to the vast success of bioengineering techniques, a series of large scale analysis tools has ... more Due to the vast success of bioengineering techniques, a series of large scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. The analysis of DNA microarray image consists of several steps; gridding, segmentation, and quantification that can significantly deteriorate the quality of gene expression in formation, and hence decrease our confidence in any derived research results. Thus, microarray data processing steps become critical for performing optimal microarray data analysis and deriving meaningful biological information from microarray images .Segmentation is the process, by which each individual cell in the grid must be selected to determine the spot signal and to estimate the background hybridization. In this paper, four segmentation methods are explored; "fixed circle", "adaptive circle", "thresholding", and "adaptive shape" segmentation. By comparing the results, it was found that the "adaptive shape segmentation method" can segment noisy microarray images correctly, gives high accuracy results and minimal processing time, and can be applied to various types of noisy microarray images.
International Journal of Computer Applications, 2012
Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depen... more Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.
Journal of Advanced Research, 2015
Genetics of autoimmune diseases represent a growing domain with surpassing biomarker results with... more Genetics of autoimmune diseases represent a growing domain with surpassing biomarker results with rapid progress. The exact cause of Rheumatoid Arthritis (RA) is unknown, but it is thought to have both a genetic and an environmental bases. Genetic biomarkers are capable of changing the supervision of RA by allowing not only the detection of susceptible individuals, but also early diagnosis, evaluation of disease severity, selection of therapy, and monitoring of response to therapy. This review is concerned with not only the genetic biomarkers of RA but also the methods of identifying them. Many of the identified genetic biomarkers of RA were identified in populations of European and Asian ancestries. The study of additional human populations may yield novel results. Most of the researchers in the field of identifying RA biomarkers use single nucleotide polymorphism (SNP) approaches to express the significance of their results. Although, haplotype block methods are expected to play a complementary role in the future of that field.
Support Vector Machine Based Computer Aided Diagnosis System for Large Lung Nodules Classification
Journal of Medical Imaging and Health Informatics, 2013
ABSTRACT
Computer aided detection system for micro calcifications in digital mammograms
Computer methods and programs in biomedicine, 2014
Breast cancer continues to be a significant public health problem in the world. Early detection i... more Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based...
Pigmented skin lesion diagnosis using geometric and chromatic features
2014 Cairo International Biomedical Engineering Conference (CIBEC), 2014
Skin cancer appears to be one of the most dangerous types among others by the presence of maligna... more Skin cancer appears to be one of the most dangerous types among others by the presence of malignant melanoma as one of pigmented skin lesion forms. Automated system for the purpose of pigmented skin lesion diagnosis mentioned through that paper is recommended as a non-invasive diagnosis tool. To obviate the problem of qualitative interpretation, two different image sets are used to examine the proposed system, a set of images acquired by standard camera (clinical images) and another set of dermoscopic images captured from the magnified dermoscope. Images are enhanced and segmented to separate the lesion from the background. Different geometric and chromatic features are extracted from the region of interest resulting from segmentation process. Then, the most prominent features that can cause an effect are selected by different selection methods; which are the Fisher score ranking and the t-test method. Most prominent features were introduced to two different classifiers; artificial neural network and Support vector machine for the discrimination of the two groups of lesions. System performance was measured regarding Specificity, Sensitivity and Accuracy. The artificial neural network designed with the combined geometric and chromatic features selected by fisher score ranking enabled a diagnostic accuracy of 95% for dermoscopic and 93.75% for clinical images.
Journal of Bioinformatics and Intelligent Control, 2012
Hepatocellular carcinoma consider one of the common malignant tumors in the world, chronic hepati... more Hepatocellular carcinoma consider one of the common malignant tumors in the world, chronic hepatitis and liver cirrhosis have been recognized as important risk factors for the development of hepatocellular carcinoma. Hepatocellular carcinoma is the third most common cause of cancer mortality worldwide. The outcome of hepatocellular carcinoma patients still dismal because it is difficult to detect the disease at its early stage. Genomic DNA copy number alterations are associated with many complex diseases such as hepatocellular carcinoma. Copy number alterations are important for both the basic understanding of hepatocellular carcinoma and its diagnosis. Array-based comparative genomic hybridization is a technique used to identify copy number changes in genomic DNA .The aim of this work is to apply discrete stationary wavelet transform technique to a number of human chromosomes for analyzing array-based comparative genomic hybridization data to evaluate the prognosis of hepatocellular carcinoma patients in order to identify genome-wide alternations in copy number from the genomic data. We tested 67 samples of hepatocellular carcinoma patients on specific chromosome regions, then applied stationary wavelet transform algorithm to detect genomic DNA alternations in copy number. Our results show that a gain of 1q was detected in 55% and a gain of 20q was detected in 67% of hepatocellular carcinoma cases. Also, a loss of 4q was detected in 67%, a loss and gain of 8q, 8p was detected in 87%, a loss of 13q was detected in 72%, loss in 16q was detected in 75%, and loss of 17q was detected in 33% of hepatocellular carcinoma cases.
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Papers by Mai Mohamed Said Mabrouk