Cancer remains a leading cause of mortality worldwide, with early detection being critical for improving survival rates. Traditional diagnostic methods, su

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.

Promoters are the basic functional cis-elements to which RNA polymerase binds to initiate the process of gene transcription. Comprehensive understanding gene expression and regulation depends on the precise identification of promoters, as they are the most important component of gene expression. This study aimed to develop a machine learning-based model to predict promoters in Klebsiella aerogenes (K. aerogenes). In the prediction model, the promoter sequences in K. aerogenes genome were encoded by pseudo k-tuple nucleotide composition (PseKNC) and position-correlation scoring function (PCSF). Numerical features were obtained and then optimized using mRMR by combining with support vector machine (SVM) and 5-fold cross-validation (CV). Subsequently, these optimized features were inputted into SVM-based classifier to discriminate promoter sequences from non-promoter sequences in K. aerogenes. Results of 10-fold CV showed that the model could yield the overall accuracy of 96.0% and the area under the ROC curve (AUC) of 0.990. We hope that this model will provide help for the study of promoter and gene regulation in K. aerogenes.

As the population ages, intestinal health in the elderly has become a key area of concern, with gut microbiota dysbiosis emerging as a significant issue. This review summarizes the factors influencing dysbiosis and interventions from both traditional Chinese medicine (TCM) and Western medicine, offering a reference for future research. A comprehensive search of global databases up to March 2024 identified 617 original studies on gut microbiota dysbiosis in individuals aged 65 and older. After applying strict PRISMA guidelines, 20 articles met the inclusion criteria. Key findings are summarized in four areas: 1) the definition and mechanisms of dysbiosis, 2) evaluation tools for gut microbiota imbalance, 3) factors contributing to dysbiosis in the elderly, and 4) pharmacological treatments. Both TCM and Western medicine offer unique advantages in managing gut microbiota dysbiosis, and the choice of intervention should be tailored to the individual’s condition. Future research should focus on optimizing integrated TCM and Western medicine approaches to improve outcomes for elderly patients with gut microbiota dysbiosis.

Umami peptides are naturally occurring bioactive peptides with distinctive umami taste and promising applications in the food industry. However, conventional identification methods based on sensory evaluation, chromatography, and mass spectrometry are time-consuming, costly, and low-throughput. Recent advances in artificial intelligence (AI) have enabled efficient computational frameworks for large-scale umami peptide screening. This review summarizes recent progress in AI-driven umami peptide prediction and mechanistic insights from molecular docking analyses. We overview available umami peptide databases, feature representation strategies, and state-of-the-art AI models, including machine learning, deep learning, and multi-model fusion approaches. In addition, molecular docking studies elucidating the interactions between umami peptides and taste receptors, particularly T1R1/T1R3, are discussed to support rational peptide design. Finally, current challenges and future perspectives in AI-assisted umami peptide research are highlighted, providing guidance for the intelligent development and application of umami peptides in the food industry.