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Purpose Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease. Methods Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children’s nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model. Results The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers. Conclusion The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.

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N-linked glycosylation is one of the most common protein post-translation modifications (PTMs) in humans where the Asparagine (N) amino acid of the protein is attached to the glycan. It is involved in most biological processes and associated with various human diseases as diabetes, cancer, coronavirus, influenza, and Alzheimer's. Accordingly, identifying N-linked glycosylation sites will be beneficial to understanding the system and mechanism of glycosylation. Due to the experimental challenges of glycosylation site identification, machine learning becomes very important to predict the glycosylation sites. This paper proposes a novel N-linked glycosylation predictor based on bagging positive-unlabeled (PU) learning and stacking ensemble machine learning (PUStackNGly). In the proposed PUStackNGly, comprehensive sequence and structural-based features are extracted using different feature extraction descriptors. Then, ensemble-based feature selection is employed to select the most significant and stable features. The ensemble bagging PU learning selects the reliable negative samples from the unlabeled samples using four supervised learning methods (support vector machines, random forest, logistic regression, and XGBoost). Then, stacking ensemble learning is applied using four base classifiers: logistic regression, artificial neural networks, random forest, and support vector machine. The experiments results show that PUStackNGly has a promising predicting performance compared to supervised learning methods. Furthermore, the proposed PUStackNgly outperforms the existing N-linked glycosylation prediction tools on an independent dataset with 95.11% accuracy, 100% recall 80.7% precision, 89.32% F1 score, 96.93% AUC, and 0.87 MCC.