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Recently, the numerical optimization field has attracted the research community to propose and develop various metaheuristic optimization algorithms. This paper presents a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA). The proposed algorithm is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for solving optimization problems. The dynamic search behavior of honey badger with digging and honey finding approaches are formulated into exploration and exploitation phases in HBA. Moreover, with controlled randomization techniques, HBA maintains ample population diversity even towards the end of the search process. To assess the efficiency of HBA, 24 standard benchmark functions, CEC’17 test-suite, and four engineering design problems are solved. The solutions obtained using the HBA have been compared with ten well-known metaheuristic algorithms including Simulated annealing (SA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Success-History based Adaptive Differential Evolution variants with linear population size reduction (L-SHADE), Moth-flame Optimization (MFO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Thermal Exchange Optimization (TEO) and Harris hawks optimization (HHO). The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration–exploitation balance, as compared to other methods used in this study. The source code of HBA is currently available for public at https://www.mathworks.com/matlabcentral/fileexchange/98204-honey-badger-algorithm .

Multiple sclerosis is an autoimmune disease that causes psychological impacts and severe physical disabilities, including motor disabilities and partial blindness. This work introduces an early detection method for multiple sclerosis disease by analyzing transcriptomic microRNA data. By transforming this phenotype classification problem into a text mining problem, multiple sclerosis disease biomarkers can be obtained. To our knowledge, text mining methods have not been introduced previously in transcriptomic data analysis of multiple sclerosis disease. Hence, this work presents a complete predictive model by combining consecutive transcriptomic data preprocessing procedures, followed by the proposed KmerFIDF method as a feature extraction method and linear discriminant analysis for dimensionality reduction. Predictive machine learning methods can then be obtained accordingly. This study describes experimental work on a transcriptomic dataset of noncoding microRNA sequences denoted from relapsing-remitting multiple sclerosis patients before fingolimod treatment and after six consecutive months of treatment. The experimental results of the predictive methods with the proposed model report sensitivity, specificity, F1-score, and average accuracy scores of 96.4, 96.47, 95.6, and 97% with random forest, 92.89, 92.78, 93.2, and 94% with support vector machine and 91.95, 92.2, 93.1, and 94% with logistic regression, respectively. These promising results support the introduced model and the proposed KmerFIDF method in transcriptomic data analysis. Moreover, comparative experiments are conducted with two referenced studies. The obtained results show that the average reported accuracy scores of the proposed model outperform the referenced literature work.

Multiple sclerosis disease is a main cause of non-traumatic disabilities and one of the most common neurological disorders in young adults over many countries. In this work, we introduce a survey study of the utilization of machine learning methods in Multiple Sclerosis early genetic disease detection methods incorporating Microarray data analysis and Single Nucleotide Polymorphism data analysis and explains in details the machine learning methods used in literature. In addition, this study demonstrates the future trends of Next Generation Sequencing data analysis in disease detection and sample datasets of each genetic detection method was included .in addition, the challenges facing genetic disease detection were elaborated.

Several metaheuristic optimization algorithms have been developed to solve the real-world problems recently. This paper proposes a novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems. Henry’s law is an essential gas law relating the amount of a given gas that is dissolved to a given type and volume of liquid at a fixed temperature. The HGSO algorithm imitates the huddling behavior of gas to balance exploitation and exploration in the search space and avoid local optima. The performance of HGSO is tested on 47 benchmark functions, CEC’17 test suite, and three real-world optimization problems. The results are compared with seven well-known algorithms; the particle swarm optimization (PSO), gravitational search algorithm (GSA), cuckoo search algorithm (CS), grey wolf optimizer (GWO), whale optimization algorithm (WOA), elephant herding algorithm (EHO) and simulated annealing (SA). Additionally, to assess the pairwise statistical performance of the competitive algorithms, a Wilcoxon rank sum test is conducted. The experimental results revealed that HGSO provides competitive and superior results compared to other algorithms when solving challenging optimization problems.

This Electronic Health (E-Health) is a broad expression that enables the communication between healthcare professionals in handling patient information through the cloud. Exchanging medical data over the public cloud requires securing transferring for the data that's direct many researchers in proposing different secure schemes to enable users to handle data safely without hacking or alternating. In this study, one of the most common encryption algorithms called Triple Data Encryption Standard (Triple-DES) has been implemented with the aid of the hash function and DNA cryptography base to encrypt different biosignals into DNA format to add a security layer for the proposed system.

Background The computational biology approach has advanced exponentially in protein secondary structure prediction (PSSP), which is vital for the pharmaceutical industry. Extracting protein structure from the laboratory has insufficient information for PSSP that is used in bioinformatics studies. In this paper, the support vector machine (SVM) model and decision tree are presented on the RS126 dataset to address the problem of PSSP. A decision tree is applied for the SVM outcome to obtain the relevant guidelines possible for PSSP. Furthermore, the number of produced rules was fairly small, and they show a greater degree of comprehensibility compared to other rules. Several of the proposed principles have compelling and relevant biological clarification. Results The results confirmed that the existence of a particular amino acid in a protein sequence increases the stability for the forecast of protein secondary structure. The suggested algorithm achieved 85% accuracy for the E|~E classifier. Conclusions The proposed rules can be very important in managing wet laboratory experiments intended at determining protein secondary structure. Lastly, future work will focus mainly on large protein datasets without overfitting and expand the amount of extracted regulations for PSSP.

Computer-aided diagnosis (CAD) approach is presented as strong frameworks to solve the inaccuracy problems. The major purpose of this paper is to improve a CAD system depended on supervised classification that can be useful in diagnosing and detecting the changes of breast cancers in digitized mammograms earlier, accurately and faster than standard examination programs by applying CAD according to image processing techniques beginning with preprocessing step, segmentation, feature extraction and finally classification stage. The work presented in this study is based on the integration of different features such as shape, texture and invariant moment features. This integration achieved best results for sensitivity and specificity rather than using the one type of features in breast cancer classification. The accuracy of our integration system reached 96% in the automatic mode of ANN while best accuracy accomplished by features result according to invariant moments that reached 97% by ANN in an automatic way. Keywords: Computer-aided diagnosis (CAD), Mammograms, K-nearest neighbor classifier (KNN), Support vector machine (SVM), Artificial neural network (ANN)

Background: Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery. Methods: Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach. Results: Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum. Conclusion: Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

DNA computing is an area of natural computing inspired by molecular biology. It can be used to perform arithmetic and logic operations on information encoded as DNA strands. Due to the rapid development of network technology, E-health care services are helpful for all patients from anywhere at any time. Patients can use these services through a public channel. The security of patients’ privacy is therefore an essential issue in E-health care. In this paper, an encryption scheme is proposed for hiding patient information in a medical image using the Least Significant Bit algorithm, which involves hiding data in the least bit of image pixels. The image is then compressed with a key which is generated from a combination of six stages of chaotic maps and DNA encoding rules that result in encoding data into a DNA format. The proposed security system was applied to six different medical images, including X-ray images, CT images, and MRI images. The results have been evaluated using several evaluation metrics. The proposed scheme appears to be robust.

The difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes’ Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC’17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris’ hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/79822-archimedes-optimization-algorithm

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS, or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible, and that it might serve useful functions. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. BCI systems include machine learning algorithms (MLA). Their performance depends on the feature extraction and classification techniques employed. This work discusses the performance of different machine learning algorithms. First, a preprocessing step is introduced to the subjects to extract the important features before applying the machine learning algorithms. The presented algorithms are linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), and Bayesian linear discriminant analysis (BLDA). It is found that BLDA and SVMIV classifiers yield the highest performance for both subjects considered in our study.

Cancer is considered one of the most dangerous diseases leading to imminent death during recent decades. It spreads quickly at a frightening pace to all parts of the body and early detection of such diseases may help reduce the risks. Many methods have been used to classify and predict the outcome of cancer, such as image processing techniques, artificial intelligence, and nanotechnology. Out of these methods, artificial intelligence techniques have played an essential role and have provided satisfying results from different investigations in various fields. Considering that cancer is a genetic disease, electron–ion interaction pseudo potential can be used to convert DNA sequences from string shape into number values so that genomic signal processing can be applied for the feature extraction step. In this paper, 51 healthy and 51 cancer genes from multiple cells obtained from the National Center for Biotechnology Information Genbank were used for analysis. Then, 7 invariant moments were extracted into 2 types of learning machine methods: supervised and unsupervised learning. Finally, their results were compared using Receiver Operating Characteristic parameters. From these results, the best accuracy was obtained from the trainable cascade-forward backpropagation network. The calculated parameters were 0.3333, 0.3158, 0.6842, 0.6667, and 67.5% for the Kohonen network, 0.1053, 0.1429, 0.8571, 0.8947, and 87.5% for the feed-forward network, and 0.0000, 0.0909, 0.9091, 1.0000, and 95% for the cascade-forward network for FPR, FNR, TPR, TNR, and accuracy respectively.

Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

In this paper, recent algorithms are suggested to repair the issue of motif finding. The proposed algorithms are cuckoo search, modified cuckoo search and finally a hybrid of gravitational search and particle swarm optimization algorithm. Motif finding is the technique of handling expressive motifs successfully in huge DNA sequences. DNA motif finding is important because it acts as a significant function in understanding the approach of gene regulation. Recent results of existing motifs finding programs display low accuracy and can not be used to find motifs in different types of datasets. Practical tests are implemented first on synthetic datasets and then on benchmark real datasets that are based on nature-inspired algorithms. The results revealed that the hybridization of gravitational search algorithm and particle swarm algorithms provides higher precision and recall values and provides average enhancement of F-score up to 0.24, compared to other existing algorithms and tools, and also that cuckoo search and modified cuckoo search have been able to successfully locate motifs in DNA sequences.

The DNA motif discovery (MD) problem is the main challenge of genome biology, and its importance is directly proportional to increasing sequencing technologies. MD plays a vital role in the identification of transcription factor binding sites that help in learning the mechanisms for regulation of gene expression. Metaheuristic algorithms are promising techniques for eliciting motif from DNA genomic sequences, but often fail to demonstrate robust performance by overcoming the inherent challenges in complex gene sequences, making search environment extremely non-convex for optimization methods. This paper proposes a novel modified Henry gas solubility optimization (MHGSO) algorithm for motif discovery which elicits a functional motif in DNA genomic sequences. In our approach, a new stage that captures the main characteristics of the motifs in DNA sequences is proposed, and MHGSO imitates the motifs characteristics for accurate detection of target motif. The performance of the MHGSO algorithm is validated using both synthetic and real datasets. Results confirm the stability and superiority of the proposed algorithm compared to state-of-the-art algorithms including MEME, DREME, XXmotif, PMbPSO, and MACS. Based on several evaluation matrices, MHGSO outperforms the competitor techniques in terms of nucleotide-level correlation coefficient, recall, precision, F-score, Cohen’s Kappa, and statistical validation measures.

Computerized analysis of pigmented skin lesions (PSLs) is a lively space of survey that dates back over 25 years. Recently, different automated computer-based systems stand to be a helpful tool. Physicians’ usage for ABCD worldwide as the main tool of diagnosis and self-examination make it the common reference for different skin cancer diagnosis models. This system is comprised of the main four key warning signs of the ABCD model that can be detected by visual inspection and more accurately identified by the automated system to diagnose melanoma. Based on the image area identified as PSL, through pre-processing and segmentation step, the features will then be detected regarding ABCD rule. According to what ABCD stands for, the proposed study extracts Asymmetry, Border and Color features, in addition to various parameters introduce parameter “D.” Finally, as the worldwide definition of ABCD rule of cancer diagnoses was discussed, this research also makes the final decision according to the Total Dermoscopic Score (TDS) Index, in addition to another three popular machine learning classifiers. ANN, SVM, and K-nearest neighbor were used for classification of the segmented lesions in addition to the traditional TDS. This research shows perfect results for calculating the ABCD score automatically, which reflects its viability. Different experiments developed in regard to features variety and different classification methods to reach 98.1%, 95%, and 98.75% classification accuracy when dermoscopic images were classified by TDS, Automatic ANN, and linear SVM, respectively, where the clinical images reached perfect accuracy 100% when classified by linear SVM, and very promising result 98.75% as per automatic ANN. This system considered to be the first promising digitalized system for traditional TDS regarding the achieved accuracy and using of a simple Graphical User Interface (GUI) to facilitate user easy use.

Lumbar total disc replacement (LTDR) is a surgical procedure for the treatment of degenerative disc disease (DDD) and lumbar spinal distortion to preserve range of motion (ROM). The SB Charite™ is the first device for LTDR but it produces more complications and long-term issues, causing a shortage of SB Charite™ disc. Currently, the evolution of lumbar disc arthroplasty based on some criteria like prosthetics structure, biomechanical model, tissue engineering and biomaterials approach. The optimal biomechanical model is based on kinematic and kinetic parameters together to ensure a long-term implantation with a lower rate of damage from disc implant in the spinal column. The finite element method (FEE) of human lumbar spinal is advanced to support biomechanical modeling techniques which are related to biomaterials guideline for choosing better material for implantation. Therefore, this paper presented a 3D biomechanical FEM model of L1 to L3 lumbar spines by different types of discs and materials. We applied a compressive force, and compressive force plus extension moment to this model to calculate Tresca stress of annulus fibers, strain and von Mises stress on the vertebral endplate at each intervertebral level under COMSOL Multiphysics® software.

The comprehensive framework for analyzing brain images performs by the integration between three dimensional (3D) reconstruction and neuroimaging approach to realize brain diseases progressions. Computer Aided Detection (CAD) technology has numerous achievements in brain tumor processing for improving the quality of brain visualization to support neuroradiologists without the need for surgical biopsy or resection. Despite the advance in the radiological diagnosis of neuroimaging data, magnetic resonance imaging (MRI) has some restrictions that related to human errors and incomplete interpretation of brain tumor regions. Also, MRI scan produces 2D images of the brain that was very difficult to handle different types of tumor. Therefore, many algorithms are used computer-based classification to accurately distinguish between tumor regions from the brain MR images that provided an early diagnosis of brain diseases. This study investigated the CAD system using 3D image reconstruction of MR brain and tumor structures efficiently under MATLAB platform to recognize the location, volume, and type of brain tumors. In addition, the proposed system applied the Fuzzy C-Means (FCM) algorithm as image segmentation and support vector machine (SVM) as image classification for tumor detection of MR brain images. Results confirmed that this 3D model depicted an advanced view for estimating of human brain diseases.

Computer aided detection (CAD) has numerous achievements in medical image processing field which represents a communication. Despite the advance in the radiological diagnosis of neuroimaging, magnetic resonance imaging (MRI) has some restrictions related to human errors and incomplete interpretation of brain tumour regions. Also, MRI produces 2D images of the brain that are very difficult to handle different types of tumour. This study investigated CAD system using 3D image reconstruction of MR brain and tumour structures efficiently. In addition, the proposed system applied the fuzzy C-means (FCM) algorithm as image segmentation, and support vector machine (SVM) as image classification for tumour detection of MR brain images. The results confirmed that this 3D model supported the advanced view of human brain diseases. The accurate and quantitative research results are expected to enhance the healthcare technology.

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI). Working towards this purpose, existing research in EOG based Human Computer Interaction (HCI) applications, must be organized and surveyed in order to develop a vision on the potential benefits of combining both input modalities and give rise to new designs that maximize these benefits. Our aim is to support and inspire the design of new hBCI systems based on both EEG and EOG signals, in doing so; first the current EOG based HCI systems were surveyed with a particular focus on EOG based systems for communication using virtual keyboard. Then, a survey of the current EEG-EOG virtual keyboard was performed highlighting the design protocols employed. We concluded with a discussion of the potential advantages of combining both systems with recommendations to give deep insight for future design issues for all EEG-EOG hBCI systems. Finally, a general architecture was proposed for a new EEG-EOG hBCI system. The proposed hybrid system completely alters the traditional view of the eye movement features present in EEG signal as artifacts that should be removed; instead EOG traces are extracted from EEG in our proposed hybrid architecture and are considered as an additional input modality sharing control according to the chosen design protocol.

Embryonic stem cells (ESC) are pluripotent cells, had been obtained from an embryo in a few days old in which all 200+ kinds of tissue in the human body originate a fact which makes them potentially important in medicine. DNA methylation on the other hand plays an important role in gene modification leading to change in biological processes as pluripotent and differentiated cells or reasoned for many diseases. Cytosines in CpG dinucleotides can be methylated leading to concentrate to predict methylation in CpG loci inside DNA by helping of results coming from whole genome bisulfite sequencing (WGBS) experiment but very expensive and Illumina 450K array experiment that covers less than 2% of CpG locus. A machine learning model has been implemented using support vector machine classifier (SVM) that is trained on chromosome 18 of the human H1 ESC with both 450K array data and WGBS. The SVM gives an accuracy of 99% when testing on chromosome 18 of the human H9 ESC to predict methyl or un-methyl for each CpG loci in the chromosome separately.

The DNA motif discovery is an important step to study gene function. A DNA motif refers to a short similar repeated pattern of nucleotides that has biological meaning. In this paper, a bio-inspired algorithm called Grey Wolf Optimizer technique for DNA motif finding (GWOMF) is presented and evaluated on six synthetic datasets. Experimental results show that the GWOMF algorithm is more efficient than the state-of-art methods with a comparable computational time. Results confirmed the robustness of the new technique and demonstrate its value as a motif discovery tool with average success rate of 86.6%.

Cancer is one of the most dangerous diseases that the world faces and has raised the death rate in recent years, it is known medically as malignant neoplasm, so detection of it at the early stage can yield a promising approach to determine and take actions to treat with this risk. Bioinformatics is the application of computer technology to the management of biological information. Its field has now solidly settled itself as a control in molecular biology and incorporates an extensive variety of branches of knowledge from structural biology, genomics to gene expression studies. Genomic signal processing (GSP) techniques have been connected most all around in bioinformatics and will keep on assuming an essential part in the investigation of biomedical issues. It refers to using the digital signal processing (DSP) methods for genomic data (e.g. DNA sequences) analysis. Recently, GSP is one of important methods which can be used to detect the cancerous cells that are often caused due to genetic abnormality. In this paper, some of GSP techniques that depend on frequency domain transformation are presented. These techniques are: Discrete Fourier Transform (DFT), Power Spectral Density (PSD) of DFT and PSD obtained by Welch's averaged periodogram method. The proposed method has given satisfied results for differentiation between normal and cancerous cells. The algorithm is tested on six healthy and six cancerous genes of breast cell which are obtained from NCBI genbank.

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 on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications 'patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.

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.

Motifs are short patterns in Deoxyribonucleic Acid (DNA) that indicate the presence of certain biological characteristics. Motifs finding is the process of successfully finding meaningful motifs in large DNA sequences. Nature-inspired algorithms have been recently gaining much popularity in solving complex and large real-world optimization problems similar to the motif finding problem. This work aims on investigating the application of nature-inspired algorithms in motif finding problem. The investigation methodology is divided into three main approaches; the first is to apply well-known nature-inspired algorithms in solving the problem, then the enhancement of an algorithm is investigated, and finally the hybridization between two algorithms is investigated. Experiments are performed on synthetic as well as real data sets. The results show that the combination provides the best results, however, individual and modified algorithms provide also good results compared to some state-of-the-art tools.

cancer represents one of the greatest medical causes of mortality. The majority of Hepatocellular carcinoma arises from the accumulation of genetic abnormalities, and possibly induced by exterior etiological factors especially HCV and HBV infections. There is a need for new tools to analysis the large sum of data to present relevant genetic changes that may be critical for both understanding how cancers develop and determining how they could ultimately be treated. Gene expression profiling may lead to new biomarkers that may help develop diagnostic accuracy for detecting Hepatocellular carcinoma. In this work, statistical technique (discrete stationary wavelet transform) for detection of copy number alternations to analysis high-density single-nucleotide polymorphism array of 30 cell lines on specific chromosomes, which are frequently detected in Hepatocellular carcinoma have been proposed. The results demonstrate the feasibility of whole-genome fine mapping of copy number alternations via high-density single-nucleotide polymorphism genotyping, Results revealed that a novel altered chromosomal region is discovered; region amplification (4q22.1) have been detected in 22 out of 30-Hepatocellular carcinoma cell lines (73%). This region strike, AFF1 and DSPP, tumor suppressor genes. This finding has not previously reported to be involved in liver carcinogenesis; it can be used to discover a new HCC biomarker, which helps in a better understanding of hepatocellular carcinoma.

DNA microarray is a powerful tool and is widely used in genetics to monitor expression levels of thousands of genes in parallel. The gene expression process consists of three stages: gridding, segmentation and quantification. Grid- ding deals with finding areas in the microarray image which contain one spot using grid lines. This step can be done ma- nually or automatically. In this paper, we propose an efficient and simple automatic gridding method for microarray image analysis. This method was implemented using MATLAB software and found very effective for gridding arrays with low intensity, poor quality spotsand tested by a number of microarray images. Results show that this method gives high accu- racy of 76.9% improved to 98.6% when a preprocessing step is considered, rendering the method a promising technique for an efficient and automatic gridding the noisy microarray images.

The progress in the field of electronics and technology as well as the processing of signals coupled with advance in the use of computer technology has given the opportunity to record and analyze the bio-electric signals from the human body in real time that requires dealing with many challenges according to the nature of the signal and its frequency. This could be up to 1 kHz, in addition to the need to transfer data from more than one channel at the same time. Moreover, another challenge is a high sensitivity and low noise measurements of the acquired bio-electric signals which may be tens of micro volts in amplitude. For these reasons, a low power wireless Electromyography (EMG) data transfer system is designed in order to meet these challenging demands. In this work, we are able to develop an EMG analogue signal processing hardware, along with computer based supporting software. In the development of the EMG analogue signal processing hardware, many important issues have been addressed. Some of these issues include noise and artifact problems, as well as the bias DC current. The computer based software enables the user to analyze the collected EMG data and plot them on graphs for visual decision making. The work accomplished in this study enables users to use the surface EMG device for recording EMG signals for various purposes in movement analysis in medical diagnosis, rehabilitation sports medicine and ergonomics. Results revealed that the proposed system transmit and receive the signal without any losing in the information of signals.

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.

Pigmented skin lesions are the normal part of the skin, however its anomalous appearance is an annoying sign due to the presence of melanomas one of its malignant forms. Although melanoma is a deadly considerable disease, its early detection is a serious step toward mortality reduction. The proposed research discusses the outcome of introducing312 different features in a non-invasive diagnosis method for pigmented skin lesion diagnosis. To obviate the problem of qualitative interpretation, two different image sets are utilized 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. System contribution appears in using large conclusive set of features fed to different classifiers composing totally complete, new and different approaches for the purpose of disease diagnosis. Miscellaneous types of features used such as geometric, chromatic, and texture features extracted from the region of interest resulted from segmentation process. Then, the most prominent features that can cause an effect are selected by three different methods; Fisher score method, t-test, and F-test. The high-ranking features are used for the diagnosis of the two lesion groups using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) as three different classifiers proposed. System performance was measured in regards Specificity, Sensitivity and Accuracy. The ANN designed with the feature selected according to fisher score method enables a diagnostic accuracy of 96. 25% and 97% for dermoscopic and clinical images respectively.

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.

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.

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.

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.

Copy number changes or alterations are a form of genetic variation in the human genome. Genomic DNA copy number alterations (CNAs) are associated with the development and progression of cancers. Array-based comparative genomic hybridization (a-CGH) is a technique used to identify copy number changes in genomic DNA. It yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of automated statistics algorithms for learning about the genomic alterations from array CGH data. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. For this purpose the proposed study introduces three different approaches; Circular binary segmentation, Bayesian approach, relying on the hidden Markov model and effective Gaussian mixture (GM) clustering for the analysis of array CGH profiles. Publicly available data on pancreatic adenocarcinoma and Coriell cell line bacterial artificial chromosome (BAC) array were used for the analysis to illustrate the reliability and success of the techniques.

DNA Microarray is an innovative tool for gene studies in biomedical research, and its applications can vary from cancer diagnosis to human identification. In microarray experiment, the gene expression process is divided into three basic steps: gridding, segmentation, and quantification. 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 preprocessing, 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.

Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and wet macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the exudates, the blood vessels, the optic disc and the macula and the region between them. The earlier the detection of exudates in fundus images, the stronger the kept sight level. So, early detection of exudates in fundus images is of great importance for early diagnosis and proper treatment. In this paper, we provide a feature-based method for early detection of exudates. The method is based on segmenting all objects that have contrast with the background including the exudates. The exudates could then be extracted after eliminating the other objects from the image. We proposed a new method for extracting the blood vessel tree based on simple morphological operations. The circular structure of the optic disc is obtained using Hough transform. The regions representing the blood vessel tree and the optic disc are set to zero in the segmented image to get an initial estimate of exudates. The final estimation of exudates are obtained by morphological reconstruction. This method is shown to be promising as we can detect the very small areas of exudates.

Protein-ligand interaction plays an important role in structural-based drug designing. The aim of this work is to select new possible candidates for HCC by in-silico drug design using bioinformatics techniques and tool. Essential proteins were targeted for HCC metastasis; drugs were designed for them by using ligand-based drug design based on active model drugs. The study considered BCL-XL and FGF proteins and the commercially available drugs against HCC. The receptor was docked to those drugs and the energy values were obtained using the Molecular Operating Environment (MOE) docking software. According to the obtained energy values, we have chosen the best drugs. Also, the aim was to improve the binding efficiency and steric compatibility of the obtained drug by improving the Absorption Distribution Metabolism Excretion Toxicity (ADMET) properties of the analogues using available in-silico ADMET tools. The results of molecular docking identified 10 candidates for FGF and 17 candidates for BCL-XL. After the ADMET studies, these candidates are reduced to only 2 best candidates for FGF and 1 best candidate for BCL-XL.