METHODS: We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs' prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature.
RESULTS: When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively.
CONCLUSION: Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.
METHODS: Hair cortisol concentration was measured in 307 autistic children and 282 non-autistic controls aged between 2 and 17 years recruited from four Australian states who participated in providing hair samples and demographic data to the Australian Autism Biobank. Independent samples t-test or one-way analysis of variance (ANOVA) were conducted to determine significant differences in the mean hair cortisol concentration (pg/mg) between potential covariates. Primary analysis included multivariable regression modelling of the collapsed sample to identify variables that were significantly associated with hair cortisol concentration after controlling for covariates. We also accounted for the potential interaction of multiple biological (e.g., age, sex, BMI) and psychosocial characteristics at the level of the child, the mother and the father, and the family unit.
RESULTS: Our findings suggest that the diagnosis of autism was not a significant predictor of chronic stress, as measured by hair cortisol concentration. However, findings of the multivariable regression analysis showed that key factors such as area of residence (Queensland vs Victorian state of residence) and decrease in child's age were significantly associated with higher hair cortisol concentration whereas lower family income was significantly associated with higher hair cortisol concentration.
CONCLUSION: To our knowledge, this is the first study to show that socioeconomic factors such as family annual income affect hair cortisol status in autistic children, indicating that the psychosocial environment may be a potential mediator for chronic stress in autistic children just as it has been demonstrated in non-autistic children.