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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

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.

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.

Parkinson's disease (PD) is a complex neurodegenerative disorder with a substantial genetic component. Over the past decade, genome-wide association studies (GWAS) have identified numerous loci associated with PD risk; however, interpretation of these findings and their broader applicability remain challenging. In this systematic review, we synthesize results from 35 GWAS published between 2015 and 2025, encompassing diverse study designs and ancestries. Recurrent risk loci, including SNCA, LRRK2, MAPT, and GBA1, were consistently replicated across multiple studies, while several ancestry-specific associations were reported, particularly in East Asian and African ancestry cohorts. Nevertheless, representation of African, South Asian, and Latino populations remains limited, constraining the global generalizability of current findings. We also discuss methodological extensions beyond single-variant GWAS, including rare variant analyses, polygenic risk scores, and machine learning-based approaches, which have been applied to complement traditional analyses but remain primarily research tools due to limited validation and interpretability. Together, this review outlines the current genetic landscape of PD and identifies key methodological and population-based gaps that must be addressed to support robust and equitable translation of GWAS discoveries.

In drug development, managing interactions such as drug–drug, drug–disease, and drug–nutrient is critical for ensuring the safety and efficacy of pharmacological treatments. These interactions often overlap, forming a complex, interconnected landscape that necessitates accurate prediction to improve patient outcomes and support evidence-based care. Recent advances in artificial intelligence (AI), powered by large-scale datasets (e.g., DrugBank, TWOSIDES, SIDER), have significantly enhanced interaction prediction. Machine learning, deep learning, and graph-based models show great promise, but challenges persist, including data imbalance, noisy sources, Limited explainability, and underrepresentation of certain types of interactions. This systematic review of 147 studies (2018–2024) is the first to comprehensively map AI applications across major interaction types. We present a detailed taxonomy of models and datasets, emphasizing the growing roles of large language models and knowledge graphs in overcoming key limitations. Their integration—alongside explainable AI tools—enhances transparency, paving the way for AI-driven systems that proactively mitigate adverse interactions. By identifying the most promising approaches and critical research gaps, this review lays the groundwork for advancing more robust, interpretable, and personalized models for drug interaction prediction.

One of the most intricate parts of the human body is the spine. It serves multiple purposes. It supports and helps the movement of the body. It carries the weight of the entire abdomen, the upper limbs, and the head. The aim of this study is to establish a feasible 3D finite element model of the lumbar spine using the finite element method route and validate it by comparing the simulation results with data from in vitro experiments. A lumbar spine (L1–L5) finite element (FE) model was developed, and it included posterior fixation of pedicle screws (PS) at the L3–L4 segment level. This FE study investigated the impact of the posterior PS fixation system on the lumbar spine’s biomechanics using different materials for the screw-rod fixation system. Using titanium and CFR-PEEK materials, the impact of a posterior PS fixation system on lumbar spine biomechanics was examined for all physiological motions. The CFR-PEEK fixation system showed a reduction in von Misses stress at all physiological motions and an increase in the range of motion, which will increase the patient’s daily life performance rate and decrease the possibility of screw loosening and adjacent segment degeneration. The study concludes that CFR-PEEK rods are an alternate rod material to prevent the drawbacks of rigid-type rod fixation. CFR-PEEK implants have excellent mechanical stability and load-bearing capacity, reducing the likelihood of implant failure and promoting effective fusion. Results demonstrate how CFR-PEEK rods may lessen implant-related issues such as adjacent segment degeneration and screw loosening. Clinically, this could result in better long-term results for patients having lumbar fusion, decreased rates of revision surgery, and increased postoperative mobility. 

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, remains a major global health concern due to challenges in early detection and limited treatment options. Multi-omics technologies—such as genomics, proteomics, and metabolomics—enable comprehensive insights into the disease’s molecular complexity. This systematic review explores how these approaches contribute to biomarker discovery, molecular classification, and personalized treatment in HCC research. Methods: We conducted a structured review of 32 eligible studies, categorizing their computational methodologies into five primary analytical frameworks: survival analysis, unsupervised clustering, supervised machine learning, differential expression analysis, and pathway/network analysis. Notably, unsupervised clustering and supervised machine learning approaches, such as support vector machines, random forests, and deep learning models, were frequently used for subtype classification, feature selection, and predictive modeling. Results: The review identified that multi-omics approaches are widely used to discover biomarkers, classify HCC subtypes, and predict treatment responses. Common methods include clustering and machine learning. However, clinical validation remains limited, highlighting a gap in translational applicability. Conclusion: From a clinical perspective, multi-omics integration coupled with machine learning holds immense potential for improving early diagnosis, patient stratification, and therapeutic targeting. However, challenges related to data integration, interpretability, and cohort diversity must be addressed to realize this potential. This review underscores the transformative role of machine learning-enhanced multi-omics in reshaping liver cancer diagnosis and treatment and outlines future directions to bridge the gap between computational advances and clinical application.

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches.

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, TGFβ1 T869C, TNFB A252G, and VDR-ApaI, BsmI, FokI, and TaqI) were tested by genotyping patients with RA (n = 105) and unrelated controls (n = 80). Associations were tested using multiplicative, dominant, recessive, and co-dominant models. Also, the linkage disequilibrium (LD) between the VDR SNPs was measured to detect any indirect association. By comparing RA patients with controls (TNFB, BsmI, and TaqI), SNPs were associated with RA using all models. MTHFR C677T was associated with RA using all models except the recessive model. TGFβ1 and MTHFR A1298C were associated with RA using the dominant and the co-dominant models. The recessive model represented the association for ApaI variant. There were no significant differences for FokI and the presence of RA disease by the used models examination. For LD results, There was a high D' 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.

Thanks to high-throughput data technology, microRNA analysis studies have evolved in early disease detection. This work introduces two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis. Based on work the authors published previously, both introduced models involve complete pipelines of text mining methods, integrated with traditional machine learning methods, and LSTM deep learning. This work also studies the fragmentation of miRNA sequences to reduce the needed processing time and computational power. Moreover, this work studies the impact of obtaining two different library preparation kits (NEBNEXT and NEXTFLEX) on the detection accuracy for rheumatoid arthritis. Additional experiments are applied to the proposed models based on three different transcriptomic datasets. The results denote that the transcriptomic fragmentation model reported a biomarker detection accuracy of 96.45% on a sequence fragment size of 0.2, indicating a significant reduction in execution power while retaining biomarker detection accuracy. On the other hand, the LSTM model obtained a promising detection accuracy of 72%, implying savings in feature engineering processing. Additionally, the fragmentation model and the LSTM model reported 22.4% and 87.5% less execution time than work in the literature, respectively, denoting a considerable execution power reduction.