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Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century’s largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options.

Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignant gastrointestinal tumor. Ion channels contribute to tumor growth and progression through interactions with their neighboring molecules including lipids. The dysregulation of membrane ion channels and lipid metabolism may contribute to the epithelial–mesenchymal transition (EMT), leading to metastatic progression. Herein, transcriptome profiles of patients with ESCC were analyzed by performing differential gene expression and weighted gene co-expression network analysis to identify the altered ion channels, lipid metabolism- and EMT-related genes in ESCC. A total of 1,081 differentially expressed genes, including 113 ion channels, 487 lipid metabolism-related, and 537 EMT-related genes, were identified in patients with ESCC. Thereafter, EMT scores were correlated with altered co-expressed genes. The altered co-expressed genes indicated a correlation with EMT signatures. Interactions among 22 ion channels with 3 hub lipid metabolism- and 13 hub EMT-related proteins were determined using protein–protein interaction networks. A pathway map was generated to depict deregulated signaling pathways including insulin resistance and the estrogen receptor-Ca2+ signaling pathway in ESCC. The relationship between potential ion channels and 5-year survival rates in ESCC was determined using Kaplan–Meier plots and Cox proportional hazard regression analysis. Inositol 1,4,5-trisphosphate receptor type 3 (ITPR3) was found to be associated with poor prognosis of patients with ESCC. Additionally, drugs interacting with potential ion channels, including GJA1 and ITPR3, were identified. Understanding alterations in ion channels with lipid metabolism and EMT in ESCC pathophysiology would most likely provide potential targets for the better treatment of patients with ESCC.