METHODS: We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs' prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature.
RESULTS: When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively.
CONCLUSION: Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.
METHODS: A prospective analysis of ninety nine H. pylori-positive patients who underwent endoscopy in our Endoscopy suite were included in this study. DNA was isolated from antral biopsy samples and the presence of cagA, iceA, and iceA2 genotypes were determined by polymerase chain reaction and a reverse hybridization technique. Screening for H. pylori infection was performed in all patients using the rapid urease test (CLO-Test).
RESULTS: From a total of 326 patients who underwent endoscopy for upper gastrointestinal symptoms, 99 patients were determined to be H. pylori-positive. Peptic ulceration was seen in 33 patients (33%). The main virulence strain observed in this cohort was the cagA gene isolated in 43 patients. cagA was associated with peptic ulcer pathology in 39.5% (17/43) and in 28% (16/56) of non-ulcer patients. IceA1 was present in 29 patients (29%) and iceA2 in 15 patients (15%). Ulcer pathology was seen in 39% (11/29) of patients with iceA1, while 31% (22/70) had normal findings. The corresponding values for iceA2 were 33% (5/15) and 33% (28/84), respectively.
CONCLUSION: Virulence factors were not common in our cohort. The incidence of factors cagA, iceA1 and iceA2 were very low although variations were noted in different ethnic groups.
MATERIALS AND METHODS: Biofilm yield of 32 Helicobacter pylori strains (standard strain and 31 clinical strains) were determined by crystal-violet assay and grouped into poor, moderate and good biofilm forming groups. Whole genome sequencing of these 32 clinical strains was performed on the Illumina MiSeq platform. Annotation and comparison of the differences between the genomic sequences were carried out using RAST (Rapid Annotation using Subsystem Technology) and SEED viewer. Genes identified were confirmed using PCR.
RESULTS: Genes identified to be associated with biofilm formation in H. pylori includes alpha (1,3)-fucosyltransferase, flagellar protein, 3 hypothetical proteins, outer membrane protein and a cag pathogenicity island protein. These genes play a role in bacterial motility, lipopolysaccharide (LPS) synthesis, Lewis antigen synthesis, adhesion and/or the type-IV secretion system (T4SS). Deletion of cagA and cagPAI confirmed that CagA and T4SS were involved in H. pylori biofilm formation.
CONCLUSIONS: Results from this study suggest that biofilm formation in H. pylori might be genetically determined and might be influenced by multiple genes. Good, moderate and poor biofilm forming strain might differ during the initiation of biofilm formation.