Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.

Remote allergen exposure elicits eosinophil infiltration into allergen nonexposed mucosal organs and primes for allergic inflammation.

The natural history of allergic diseases suggests bidirectional and progressive relationships between allergic disorders of the skin, lung, and gut indicative of mucosal organ crosstalk. However, impacts of local allergic inflammation on the cellular landscape of remote mucosal organs along the skin:lung:gut axis are not yet known. Eosinophils are tissue-dwelling innate immune leukocytes associated with allergic diseases. Emerging data suggest heterogeneous phenotypes of tissue-dwelling eosinophils contribute to multifaceted roles that favor homeostasis or disease. This study investigated the impact of acute local allergen exposure on the frequency and phenotype of tissue eosinophils within remote mucosal organs. Our findings demonstrate allergen challenge to skin, lung, or gut elicited not only local eosinophilic inflammation, but also increased the number and frequency of eosinophils within remote, allergen nonexposed lung, and intestine. Remote allergen-elicited lung eosinophils exhibited an inflammatory phenotype and their presence associated with enhanced susceptibility to airway inflammation induced upon subsequent inhalation of a different allergen. These data demonstrate, for the first time, a direct effect of acute allergic inflammation on the phenotype and frequency of tissue eosinophils within antigen nonexposed remote mucosal tissues associated with remote organ priming for allergic inflammation.