The integration of advanced computational methods into precision medicine represents a transformative advancement in healthcare, enabling highly personalized treatment strategies based on individual genetic, environmental, and lifestyle factors. These methodologies have significantly enhanced disease diagnostics, genomic analysis, and drug discovery. However, rapid expansion in this field has resulted in fragmented understandings of its evolution and persistent knowledge gaps. This study employs a scientometric approach to systematically map the research landscape, identify key contributors, and highlight emerging trends in precision medicine. Methods: A scientometric analysis was conducted using data retrieved from the Scopus database, covering publications from 2019 to 2024. Tools such as VOSviewer and R-bibliometrix package (version 4.3.0) were used to perform co-authorship analysis, co-citation mapping, and keyword evolution tracking. The study examined annual publication growth, citation impact, research productivity by country and institution, and thematic clustering to identify core research areas. Results: The analysis identified 4574 relevant publications, collectively amassing 70,474 citations. A rapid growth trajectory was observed, with a 34.3% increase in publications in 2024 alone. The United States, China, and Germany emerged as the top contributors, with Harvard Medical School, the Mayo Clinic, and Sichuan University leading in institutional productivity. Co-citation and keyword analysis revealed three primary research themes: diagnostics and medical imaging, genomic and multi-omics data integration, and personalized treatment strategies. Recent trends indicate a shift toward enhanced clinical decision support systems and precision drug discovery. Conclusions: Advanced computational methods are revolutionizing precision medicine, spurring increased global research collaboration and rapidly evolving methodologies. This study provides a comprehensive knowledge framework, highlighting key developments and future directions. The insights derived can inform policy decisions, funding allocations, and interdisciplinary collaborations, driving further advancements in healthcare solutions.

Alpha synuclein (α-Syn) is a neuronal protein encoded by the SNCA gene and is involved in the development of Parkinson’s disease (PD). The objective of this study was to examine in silico the functional implications of non-synonymous single nucleotide polymorphisms (nsSNPs) in the SNCA gene. We used a range of computational algorithms such as sequence conservation, structural analysis, physicochemical properties, and machine learning. The sequence of the SNCA gene was analyzed, resulting in the mapping of 42,272 SNPs that are classified into different functional categories. A total of 177 nsSNPs were identified within the coding region; there were 20 variants that may influence the α-Syn protein structure and function. This identification was made by employing different analytical tools including SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, I-Mut, and HOPE. Three mutations, V82A, K80E, and E46K, were selected for further examinations due to their spatial positioning within the α-Syn as determined by PyMol. Results indicated that these mutations may affect the stability and function of α-Syn. Then, a molecular dynamics simulation was conducted for the SNCA wildtype and the four mutant variants (p.A18G, p.V82A, p.K80E, and p.E46K). The simulation examined temperature, pressure, density, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg). The data indicate that the mutations p.V82A, p.K80E, and p.E46K reduce the stability and functionality of α-Syn. These findings highlight the importance of understanding the impact of nsSNPs on α-syn structure and function. Our results required verifications in further protein functional and case–control studies. After being verified these findings can be used in genetic testing for the early diagnosis of PD, the evaluation of the risk factors, and therapeutic approaches.

The arachidonate 5-lipoxygenase activating protein (ALOX5AP) regulates leukotrienes (LTs) synthesis. LTs are involved in inflammation which is implicated in cardiovascular diseases (CVDs) and stroke. Variations in ALOX5AP gene are associated with CVDs, stroke and others because of their possible effects on ALOX5AP stability and function. In this study we investigated with molecular dynamics (MD) simulation the structural impacts of L12F, A56V, G75R, and G87R variants on ALOX5AP. We employed an array of bioinformatics techniques, including SIFT, PolyPhen-2, PANTHER, SNPs&GO, PhD-SNP, i-Mutant, MuPro, MutPred, ConSurf, and GROMACS. Results showed that the L12F variant increased structural compactness, as indicated by diminished solvent accessibility, a reduced radius of gyration, and a decrease in hydrogen bonding capacity. The A56V variant destabilized the ALOX5AP, demonstrating elevated root mean square deviation (RMSD), augmented solvent-accessible surface area, and diminished ALOX5AP compactness. The G75R and G87R variants exhibited mild effects on ALOX5AP wildtype. However, simulation trajectory snapshots results indicated G75R and G87R variants induce instability leading to structural perturbations of ALOX5AP probably due to the charge of arginine introduced by the G75R and G87R mutation. The G75R and G87R variants potentially influence ALOX5AP dynamics, stability, and function. These results require further verification in future case-control and protein functional studies.

Introduction: Thrombosis is a serious vascular disorder influenced by genetic factors, including nonsynonymous single nucleotide polymorphisms (nsSNPs) in the PROCR gene, which encodes the endothelial protein C receptor (EPCR). These mutations may disrupt EPCR stability and impair its anticoagulant function, thereby increasing the risk of thrombosis.Methods: We employed a multi-layered computational approach to analyze 217 nsSNPs in the PROCR gene. Functional impacts were predicted using Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping v2 (PolyPhen-2), Screening for Non-Acceptable Polymorphisms 2 (SNAP2), and Protein Analysis Through Evolutionary Relationships (PANTHER). Disease associations were assessed using Single Nucleotide Polymorphisms and Gene Ontology (SNP&GO) and Predictor of Human Deleterious Single Nucleotide Polymorphisms (PhD-SNP). Protein stability was evaluated using I-Mutant and MUpro, while structural implications were analyzed with Mutation Prediction (MutPred), ConSurf, and Have Our Protein Explained (HOPE). Active binding sites were identified using PyMOL. Finally, 100-nanosecond molecular dynamics (MD) simulations were conducted using GROningen MAchine for Chemical Simulations (GROMACS) to compare structural deviations, flexibility, and solvent interactions between wild-type EPCR and key mutant proteins.Results: Our integrated analysis identified three high-risk nsSNPs—T174I, N136I, and L168P—that detrimentally affect EPCR function. These variants disrupt critical glycosylation sites, α-helix integrity, and catalytic residues, leading to increased root mean square deviation (RMSD) and root mean square fluctuation (RMSF), reduced hydrogen bonding, and higher solvent-accessible surface area (SASA) in mutants compared to the wild-type. Disease association tools further linked these mutations to an elevated thrombotic risk.Discussion: These findings suggest that the identified nsSNPs destabilize EPCR by altering its structural dynamics and reducing its capacity to activate protein C. This provides mechanistic insight into how PROCR variation may contribute to thrombotic disorders and highlights the utility of in silico approaches for prioritizing potentially pathogenic variants.Conclusion: Our study demonstrates that deleterious nsSNPs in the PROCR gene can significantly impair EPCR stability and function, thereby increasing susceptibility to thrombosis. These findings provide a foundation for future experimental validation and may inform the development of personalized therapeutic strategies for managing thrombotic disorders.

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

Background: Mutations in the EPOR gene can disrupt its normal signaling pathways, leading to hematological disorders such as polycythemia vera and other myeloproliferative diseases. Methodology: In this study, a range of bioinformatics tools, including SIFT, PolyPhen-2, SNAP2, SNPs & Go, PhD-SNP, I-Mutant2.0, MuPro, MutPred, ConSurf, HOPE, and Interpro were used to assess the deleterious effects of missense nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein structure and function. Furthermore, molecular dynamics simulations (MDS) were conducted to assess the structural deviations of the identified mutant variants in comparison to the wild type. Results: The results identified two nsSNPs, R223P and G302S, as deleterious, significantly affecting protein structure and function. Both substitutions occur in functionally conserved regions and are predicted to be pathogenic, associated with altered molecular mechanisms. The MDSs indicated that while the wild-type EPOR maintained optimal stability, the G302S and R223P variants exhibited substantial deviations, adversely affecting overall protein stability and compactness. Conclusions: The computational analysis of missense nsSNPs in the EPOR gene identified two missense SNPs, R223P and G302S, as deleterious, occurring at highly conserved regions, and having substantial effects on erythropoietin receptor (EPO-R) protein structure and function, suggesting their potential pathogenic consequences.