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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.

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

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.

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.

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.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. New insights into the pathogenesis of this lethal disease are urgently needed. Chromosomal copy number alterations (CNAs) can lead to activation of oncogenes and inactivation of tumor suppressors in human cancers. Thus, identification of cancer-specific CNAs will not only provide new insight into understanding the molecular basis of tumor genesis but also facilitate the identification of HCC biomarkers using CNA. This paper presents the TMT-HCC system; it is a tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification. TMT-HCC provides researchers with a powerful way to identify and discern molecular biomarkers of HCC to inform diagnosis, prognosis, and treatment driver genes with causal roles in carcinogenesis is to detect genomic regions that under frequent alterations in cancers (CNAs). TMT-HCC also extracts protein-protein interactions from the full text of the scientific papers. The results provided that the integration of genomic and transcriptional data offers powerful potential for identifying novel cancer genes in HCC pathogenesis.

Haplotype-based methods compete with "one-SNP-at-a-time" approaches on being preferred for association studies. Chromosome 6 contains most of the known genetic biomarkers for rheumatoid arthritis (RA) disease. Therefore, chromosome 6 serves as a benchmark for the haplotype methods testing. The aim of this study is to test the North American Rheumatoid Arthritis Consortium (NARAC) dataset to find out if haplotype block methods or single-locus approaches alone can sufficiently provide the significant single nucleotide polymorphisms (SNPs) associated with RA. In addition, could we be satisfied with only one method of the haplotype block methods for partitioning chromosome 6 of the NARAC dataset? In the NARAC dataset, chromosome 6 comprises 35,574 SNPs for 2,062 individuals (868 cases, 1,194 controls). Individual SNP approach and three haplotype block methods were applied to the NARAC dataset to identify the RA biomarkers. We employed three haplotype partitioning methods which are confidence interval test (CIT), four gamete test (FGT), and solid spine of linkage disequilibrium (SSLD). P-values after stringent Bonferroni correction for multiple testing were measured to assess the strength of association between the genetic variants and RA susceptibility. Moreover, the block size (in base pairs (bp) and number of SNPs included), number of blocks, percentage of uncovered SNPs by the block method, percentage of significant blocks from the total number of blocks, number of significant haplotypes and SNPs were used to compare among the three haplotype block methods. Individual SNP, CIT, FGT, and SSLD methods detected 432, 1,086, 1,099, and 1,322 associated SNPs, respectively. Each method identified significant SNPs that were not detected by any other method (Individual SNP: 12, FGT: 37, CIT: 55, and SSLD: 189 SNPs). 916 SNPs were discovered by all the three haplotype block methods. 367 SNPs were discovered by the haplotype block methods and the individual SNP approach. The P-values of these 367 SNPs were lower than those of the SNPs uniquely detected by only one method. The 367 SNPs detected by all the methods represent promising candidates for RA susceptibility. They should be further investigated for the European population. A hybrid technique including the four methods should be applied to detect the significant SNPs associated with RA for chromosome 6 of the NARAC dataset. Moreover, SSLD method may be preferred for its favored benefits in case of selecting only one method.

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.

Bioinformatics 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. Bioinformatics is the application of computer technology to the management of biological information. 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. GSP refers to using the digital signal processing (DSP) methods for genomic data (e.g. DNA sequences) analysis. Recently, applications of GSP in bioinformatics have obtained great consideration such as identification of DNA protein coding regions, identification of reading frames, cancer detection and others. 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. GSP is a method which can be used to detect the cancerous cells that are often caused due to genetic abnormality. This systematic review discusses some of the GSP applications in bioinformatics generally. The GSP techniques, used for cancer detection especially, are presented to collect the recent results and what has been reached at this point to be a new subject of research.

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.

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. 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 H1N1 existed before using sequences from segment 8 which encodes two important proteins for immune system attack (NS1 and NS2). Our results indicate that this study have yielded a significant differences between the two types of influenza H1N1 (Pandemic and classical).

A laboratory centrifuge is a piece of laboratory equipment, driven by a motor, which spins liquid samples at high speed. There are various types of centrifuges, depending on the size and the sample capacity. Like all other centrifuges, laboratory centrifuges work by the sedimentation principle, where the centripetal acceleration is used to separate substances of greater and lesser density. The information for synthesizing the molecules that allow organisms to survive and replicate is encoded in genomic DNA. Extracted and purified genomic DNA is very important for the analysis of single nucleotide polymorphisms (SNPs), disease states, and for many other multiplex and real-time PCR applications. The aim of this work is to design a genomic DNA extraction system that satisfies downstream application needs necessary for the successful completion of experiments and able to isolate a purified genomic DNA from many sources from bacteria to humans and also is able to encompass tissues from blood to muscle and from leaf to seed. Results revealed that the proposed system works with high efficiency and spins at up to 12,000 RPM to facilitate separation of the different phases of the extraction.

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.