On leveraging self-supervised learning for accurate HCV genotyping.

Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool.

A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.

Development and multicenter validation of FIB-6: A novel, machine learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in patients with chronic hepatitis C.

BACKGROUND: Non-invasive tests (NITs), such as Fibrosis-4 index (FIB-4) and the aspartate aminotransferase-to-platelet ratio index (APRI), developed using classical statistical methods, are increasingly used for determining liver fibrosis stages and recommended in treatment guidelines replacing the liver biopsy. Application of conventional cutoffs of FIB-4 and APRI resulted in high rates of misclassification of fibrosis stages. AIM: There is an unmet need for more accurate NITs that can overcome the limitations of FIB-4 and APRI. PATIENTS AND METHODS: Machine learning with the random forest algorithm was used to develop a non-invasive index using retrospective data of 7238 patients with biopsy-proven chronic hepatitis C from two centers in Egypt; derivation dataset (n = 1821) and validation set in the second center (n = 5417). Receiver operator curve analysis was used to define cutoffs for different stages of fibrosis. Performance of the new score was externally validated in cohorts from two other sites in Egypt (n = 560) and seven different countries (n = 1317). Fibrosis stages were determined using the METAVIR score. Results were also compared with three established tools (FIB-4, APRI, and the aspartate aminotransferase-to-alanine aminotransferase ratio [AAR]). RESULTS: Age in addition to readily available laboratory parameters such as aspartate, and alanine aminotransferases, alkaline phosphatase, albumin (g/dl), and platelet count (/cm3 ) correlated with the biopsy-derived stage of liver fibrosis in the derivation cohort and were used to construct the model for predicting the fibrosis stage by applying the random forest algorithm, resulting in an FIB-6 index, which can be calculated easily at http://fib6.elriah.info. Application of the cutoff values derived from the derivation group on the validation groups yielded very good performance in ruling out cirrhosis (negative predictive value [NPV] = 97.7%), compensated advance liver disease (NPV = 90.2%), and significant fibrosis (NPV = 65.7%). In the external validation groups from different countries, FIB-6 demonstrated higher sensitivity and NPV than FIB-4, APRI, and AAR. CONCLUSION: FIB-6 score is a non-invasive, simple, and accurate test for ruling out liver cirrhosis and compensated advance liver disease in patients with chronic hepatitis C and performs better than APRI, FIB-4, and AAR.

A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19.

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.

Vitamin D Receptor Gene Polymorphisms Taq-1 and Cdx-1 in Female Pattern Hair Loss.

BACKGROUND: Female pattern hair loss (FPHL) is an important cause of hair loss in adult women and has a major impact on patient's quality of life. It evolves from the progressive miniaturization of follicles that leads to a subsequent decrease of hair density, leading to non-scarring diffuse alopecia, with characteristic clinical, dermoscopic, and histological patterns. Vitamin D receptor (VDR) is expressed in follicular keratinocytes and dermal papilla cells and is shown to have important role in hair growth and regulation of hair cycle. VDR polymorphism was not extensively investigated in hair disorders including FPHL. AIM: To investigate the association between VDR gene polymorphism (Cdx-1 and Taq-1) and FPHL to explore if these polymorphisms affect the disease occurrence or influence its clinical presentation. METHODS: A case-control study was conducted on 30 female patients with FPHL and 30 age-matched female healthy subjects, as a control group. Degree of hair loss was assessed by Ludwig grading. VDR gene polymorphisms, Taq-1 and Cdx-1 were investigated by real time polymerase chain reaction. RESULTS: CC genotype, TC genotype, and T allele of Taq-1 were more prevalent in FPHL patients than in control group. They increased disease risk by 12.6, 2.1, and 2.9 folds, respectively. AA genotype, GA genotype, and G allele of Cdx-1 were significantly more prevalent among FPHL patients than in control group. They increased disease risk by 7.5, 5.2, and 5.5 folds, respectively. CONCLUSION: Taq-1 and Cdx-1 can be considered as risk factors for FPHL. They may play role in disease persistence rather than disease initiation. This association may be explained by failure of new anagen growth and decreased proliferation of hair follicle stem cells. Further studies are recommended to confirm current findings.

Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD.

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.

Review of Different Sequence Motif Finding Algorithms.

The DNA motif discovery is a primary step in many systems for studying gene function. Motif discovery plays a vital role in identification of Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Over the past decades, different algorithms were used to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approaches that many of them are time-consuming and easily trapped in a local optimum. Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome these problems. This paper presents a general classification of motif discovery algorithms with new sub-categories that facilitate building a successful motif discovery algorithm. It also presents a summary of comparison between them.

Studying the effects of haplotype partitioning methods on the RA-associated genomic results from the North American Rheumatoid Arthritis Consortium (NARAC) dataset.

The human genome, which includes thousands of genes, represents a big data challenge. Rheumatoid arthritis (RA) is a complex autoimmune disease with a genetic basis. Many single-nucleotide polymorphism (SNP) association methods partition a genome into haplotype blocks. The aim of this genome wide association study (GWAS) was to select the most appropriate haplotype block partitioning method for the North American Rheumatoid Arthritis Consortium (NARAC) dataset. The methods used for the NARAC dataset were the individual SNP approach and the following haplotype block methods: the four-gamete test (FGT), confidence interval test (CIT), and solid spine of linkage disequilibrium (SSLD). The measured parameters that reflect the strength of the association between the biomarker and RA were the P-value after Bonferroni correction and other parameters used to compare the output of each haplotype block method. This work presents a comparison among the individual SNP approach and the three haplotype block methods to select the method that can detect all the significant SNPs when applied alone. The GWAS results from the NARAC dataset obtained with the different methods are presented. The individual SNP, CIT, FGT, and SSLD methods detected 541, 1516, 1551, and 1831 RA-associated SNPs respectively, and the individual SNP, FGT, CIT, and SSLD methods detected 65, 156, 159, and 450 significant SNPs respectively, that were not detected by the other methods. Three hundred eighty-three SNPs were discovered by the haplotype block methods and the individual SNP approach, while 1021 SNPs were discovered by all three haplotype block methods. The 383 SNPs detected by all the methods are promising candidates for studying RA susceptibility. A hybrid technique involving all four methods should be applied to detect the significant SNPs associated with RA in the NARAC dataset, but the SSLD method may be preferred because of its advantages when only one method was used.

EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface.

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.

Comparative study for haplotype block partitioning methods - Evidence from chromosome 6 of the North American Rheumatoid Arthritis Consortium (NARAC) dataset.

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.

Identification of rheumatoid arthritis biomarkers based on single nucleotide polymorphisms and haplotype blocks: A systematic review and meta-analysis.

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.

Genetic Case-Control Study for Eight Polymorphisms Associated with Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease which has a significant socio-economic 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, TGFß1 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. TGFß1 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' value between BsmI and FokI (D' = 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, TGFß1, and TNFB polymorphisms on osteoporosis in rheumatoid arthritis patients.

Diseases of the immune and the skeletal systems should be studied together for the deep interaction between them. Many studies consider osteoporosis (OP) as a risk factor for the prediction of disease progression in rheumatoid arthritis (RA). The aim of this research is to study the effect of four single nucleotide polymorphisms (SNPs) on RA patients with and without OP. The examined SNPs (MTHFR (C677T, and A1298C), TGFß1 (T869C), and TNFB (A252G)) were tested by genotyping 17 RA patients with OP and 72 RA patients without OP. Associations were tested using four models (multiplicative, dominant, recessive, and co-dominant). The studied SNPs were not significantly associated with the risk of OP in RA. MTHFR, TGFß1, and TNFB polymorphisms don't appear to be clinically useful genetic markers for predicting RA severity in Egyptian women population.

Computer aided detection system for micro calcifications in digital mammograms.

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%.

TMT-HCC: a tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification.

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.

Discovering best candidates for Hepatocellular Carcinoma (HCC) by in-silico techniques and tools.

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.

A Nonlinear Pattern Recognition of Pandemic H1N1 Using a State Space Based Methods.

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.