In recent years, antimicrobial peptides (AMPs) have attracted interest as potential peptide antibiotic due to their broad-spectrum antibacterial activity and high target specificity. However, existing research on AMP prediction mainly focuses on their functional properties, such as antibacterial, antiviral, and anticancer. This emphasis has created a significant gap in identifying AMPs that specifically target pathogens. Given the large variety of pathogens and the sparsity and imbalance of labels, it is challenging to determine which specific pathogens AMPs can effective against. To address this issue, we present PepXML, a large language model-based tool for extreme multilabel classification of pathogen-targeted AMPs. Our first step involved constructing a benchmark dataset of AMPs and their corresponding targeted pathogens, sourced from public databases. In PepXML, the peptides are embedded using ESM2. Further, clustering on a specifically designed label co-occurrence graph and hard negative sampling were employed to address challenges on data sparsity and label imbalance. To validate the reliability of our predictive results, we conducted molecular docking studies focused on peptide-bilayer membrane interactions and performed molecular dynamics simulations to elucidate the mechanisms of peptide-pathogen interactions. We anticipate that PepXML will be a valuable resource for advancing peptide-based therapeutics. The data and Python codes of the PepXML model are available at https://github.com/YannanBin/PepXML.git.

Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses of microarray data are only focused on a series of data obtained from the same lab or gene chip. Then the discoveries may only be suitable for data they experimented on but lack of general sense. In this paper, we propose a genetic programming (GP) based approach to analyze microarray datasets. The GP implements classification and feature selection at the same time. To validate the significance of the selected genes and generated classification rules, the results are tested on different datasets obtained from different experimental conditions. The results confirm the efficiency of GP in the classification of different samples.

MOTIVATION: Feature selection approaches have been widely applied to deal with the small sample size problem in the analysis of micro-array datasets. For the multiclass problem, the proposed methods are based on the idea of selecting a gene subset to distinguish all classes. However, it will be more effective to solve a multiclass problem by splitting it into a set of two-class problems and solving each problem with a respective classification system. RESULTS: We propose a genetic programming (GP)-based approach to analyze multiclass microarray datasets. Unlike the traditional GP, the individual proposed in this article consists of a set of small-scale ensembles, named as sub-ensemble (denoted by SE). Each SE consists of a set of trees. In application, a multiclass problem is divided into a set of two-class problems, each of which is tackled by a SE first. The SEs tackling the respective two-class problems are combined to construct a GP individual, so each individual can deal with a multiclass problem directly. Effective methods are proposed to solve the problems arising in the fusion of SEs, and a greedy algorithm is designed to keep high diversity in SEs. This GP is tested in five datasets. The results show that the proposed method effectively implements the feature selection and classification tasks.