The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using various machine learning algorithms, and the results indicate that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms. This finding suggests that the proposed model can provide better overall performance than other algorithms in DNA sequence classification. In addition, the proposed model is compared to two suggested algorithms, namely FLPM and PAPM, and the results show that the proposed model outperforms these algorithms in terms of accuracy and efficiency. The study further explores the impact of pattern length on the accuracy and time complexity of each algorithm. The results show that as the pattern length increases, the execution time of each algorithm varies. For a pattern length of 5, SVM Linear and EFLPM have the lowest execution time of 0.0035 s. However, at a pattern length of 25, SVM Linear has the lowest execution time of 0.0012 s. The experimental results of the proposed model show that SVM Linear has the highest accuracy and F1 score among the tested algorithms. SVM Linear achieved an accuracy of 0.963 and an F1 score of 0.97, indicating that it can provide the best overall performance in DNA sequence classification. Naive Bayes also performs well with an accuracy of 0.838 and an F1 score of 0.94. The proposed model offers a valuable contribution to the field of DNA sequence analysis by providing a novel approach to pre-processing and feature extraction. The model’s potential applications include drug discovery, personalized medicine, and disease diagnosis. The study’s findings highlight the importance of considering the impact of pattern length on the accuracy and time complexity of DNA sequence classification algorithms.

Alzheimer’s disease (AD) involves complex genetic interactions that remain challenging to model computationally. We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in AD. Our dual-pathway architecture combines: (1) linear SNP feature processing for individual genetic variants and (2) non-linear GCN analysis of functional gene networks, fused through an optimized integration module. Using rigorously curated data from the GWAS Catalog and AD-specific functional networks (FGN), the model achieved exceptional performance (accuracy: 98.04 ± 0.32%, AUROC: 0.996). Ablation studies demonstrated statistically significant contributions from both GCN (Δaccuracy − 7.92%, p < 0.001) and SNP pathways (Δaccuracy − 5.74%, p < 0.001), validating their complementary roles in AD prediction. The framework’s biological interpretability revealed known AD risk genes (APOE, PSEN1) while identifying novel network-level associations. This study advances precision medicine in neurodegeneration by providing: (i) a validated tool for early genetic risk assessment, and (ii) mechanistic insights into AD pathogenesis through network medicine paradigms. The model’s modular design permits adaptation to other complex diseases, with immediate applications in clinical trial stratification and therapeutic target discovery.

At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. Researchers are striving to improve solutions in numerous areas of computational bioinformatics as biological data grows exponentially. Faster algorithms with a low error rate are needed in real-world applications. As a result, this study offers two pattern matching algorithms that were created to help speed up DNA sequence pattern searches. The strategies recommended improve performance by utilizing word-level processing rather than character-level processing, which has been used in previous research studies. In terms of time cost, the proposed algorithms (EFLPM and EPAPM) increased performance by leveraging word-level processing with large pattern size. The experimental results show that the proposed methods are faster than other algorithms for short and long patterns. As a result, the EFLPM algorithm is 54% faster than the FLPM method, while the EPAPM algorithm is 39% faster than the PAPM method.