Biology subjects

Suchi, M. A.

Publications and source records attributed to Suchi, M. A..

2 recordsLinked to original sources

bioRxiv2025

Linking Genomic Landscape to Disease Mechanism: Core Genetic Factors Underlying Pathogenesis and Antimicrobial Resistance in Diarrheal Pathogens

BackgroundDiarrheal diseases remain a major global health burden, as they severely affect children, particularly in Bangladesh. After decades of research, the molecular mechanisms of diarrheal pathogens for disease pathogenesis and antibiotic resistance are still unknown, notably in Gram-negative bacteria. This pilot study fills the gap by employing whole genome sequencing and pan-genome analysis to analyze Bangladeshi diarrheal pathogens to identify genetic variables that cause disease pathogenesis and antibiotic resistance. ResultsHence, we investigated the genetic diversity of bacterial isolates from 31 clinical stool samples by a combination of whole-genome sequencing (WGS) and pan-genomic analysis. A core group of 50 genes, conserved across a significant number of strains, was identified via pan-genomic analysis, with considerable variation in accessory genes. This signifies a significant degree of genetic flexibility. Gene ontology analysis yielded substantial insights into prospective therapeutic targets by emphasizing the critical function of these core genes in bacterial survival and pathogenicity. Furthermore, the findings of the antimicrobial susceptibility test (AST) revealed concerning resistance trends, particularly to fluoroquinolones and beta-lactams, underscoring the necessity for enhanced surveillance and alternative therapeutic approaches. ConclusionThis study provides a comprehensive genetic framework to improve understanding of the complexity of diarrheal infections and the mechanisms underlying their resistance, fostering opportunities for potential therapeutic advancements.

microbiology↗

bioRxiv2025

APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses

Viral infections pose significant global health challenges, underscoring the urgent need for improved medications. Nevertheless, traditional medicinal approaches depend significantly on labor-intensive laboratory tests, which impede efficient identification and prolong vaccine development, particularly when screening a huge number of samples. To address these obstacles, we present a comprehensive Antiviral Peptide (AVP) Detection Dataset, comprising 14 unique features to improve the characterization of antiviral and non-antiviral peptides. Subsequently, we introduce the Antiviral Peptide detection enhanced by Ensemble Machine Learning (APDeeM) system. This advanced computational framework considerably reduces the time required for AVP detection by utilizing ensemble learning methodologies. The APDeeM system incorporates Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), and AdaBoost algorithms to facilitate the swift selection of AVP candidates without requiring urgent laboratory testing. Our proposed ensemble methodology showed superior performance, with an accuracy of 85.99%, F1 score of 87.60%, recall of 88.91%, and precision of 86.32%, exceeding the efficacy of all tested antiviral peptide prediction models in this research. The APDeeM approach signifies a substantial improvement over conventional detection techniques, expediting the identification of prospective vaccine candidates and facilitating the advancement of more effective antiviral peptides. The most promising AVP candidates may urge laboratory validation, optimize resources, and accelerate vaccine development.

bioinformatics↗