METHODS: A large-scale prospective seroepidemiological study in West and East Malaysia using the HEL-p II commercial enzyme-linked immunosorbent assay kit (AMRAD, Melbourne, Australia) to detect H. pylori antibodies. Populations surveyed in West Malaysia were a rural community from Kuala Pilah, and blood donors from Kuala Lumpur and Kota Baru. Subjects studied in East Malaysia were volunteer blood donors from Kota Kinabalu, and blood donors and healthy volunteers from Sibu. Statistical analyses using multiple logistic regression analysis were carried out to identify independent risk factors for H. pylori infection

RESULTS: A total of 2,381 subjects were evaluated. H. pylori prevalence varied from different areas of study and ranged from a low of 26.4% in blood donors from Kota Baru to a high of 55.0% in Kota Kinabalu. The most striking differences, however, were noted in the prevalence rates among different racial groups. Prevalence rates among the Malays ranged from 11.9 to 29.2%, while the Chinese ranged from 26.7 to 57.5%, and those of Indians in two studies were 49.4 and 52.3%. In every location, Malays had a significantly lower prevalence compared with the other races. The highest prevalence rates were recorded among the indigenous races in Kota Kinabalu, East Malaysia. There was no difference between males and females in the studies. An increasing trend with age was noted in the majority of studies; however, no increase in prevalence rates was noted among the Malays.

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.

RESULT: By combining SNP information from literatures, GWAS study and NCBI ClinVar, 18 unique SNPs were selected for further analysis. From these 18 SNPs, 10 SNPs came from previous study of Helicobacter pylori infection among Malay patients, 6 SNPs were from NCBI ClinVar and 2 SNPs from GWAS studies. The analysis reveals that both Royal Kelantan Malay genomes shared all the 10 SNPs identified by Maran (Single Nucleotide Polymorphims (SNPs) genotypic profiling of Malay patients with and without Helicobacter pylori infection in Kelantan, 2011) and one SNP from GWAS study. In addition, the analysis also reveals that both Royal Kelantan Malay genomes shared 3 SNP markers; HBG1 (rs1061234), HBB (rs1609812) and BCL11A (rs766432) where all three markers were associated with beta-thalassemia.