A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

<p>Abstract</p> <p>Background</p> <p>Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.</p> <p>Results</p> <p>In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in <it>S. cerevisiae </it>to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in <it>S. cerevisiae </it>(with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets.</p> <p>Conclusions</p> <p>We developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: <url>http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm</url></p

Stress hyperglycemia and risk of adverse outcomes in patients with acute ischemic stroke: a systematic review and dose–response meta–analysis of cohort studies

BackgroundStroke represents a prominent global health issue, exhibiting the third highest incidence of disability and a significant burden on both healthcare and the economy. Stress hyperglycemia, an acute reaction of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, leading to adverse outcomes and mortality. Several previous studies have indicated that stress hyperglycemia, as evaluated by the stress hyperglycemia ratio (SHR), significantly increases the risk of adverse outcomes and mortality in stroke patients. However, there is a lack of further investigation into the influence of dynamic changes in stress hyperglycemia on the clinical outcomes of acute ischemic stroke (AIS) patients. Consequently, we performed a meticulous analysis, considering dose-response relationships from existing studies, to ascertain the correlation between dynamic changes in stress hyperglycemia and the susceptibility to adverse outcomes in patients with AIS.MethodsThis investigation was prospectively registered in PROSPERO and adhered to the PRISMA guidelines. A comprehensive search was performed across English and Chinese databases. A two-sided random-effects model was employed to consolidate the odds ratios (ORs) of the highest vs. lowest categories of SHR. Restricted cubic spline (RCS) models were employed to estimate potential non-linear trends between SHR and the risk of adverse outcomes in AIS patients. Egger's test was utilized to assess publication bias. Heterogeneity was evaluated using Cochran's Q-test. The Newcastle-Ottawa Scale (NOS) tool was employed to evaluate the risk of bias of the included studies.ResultsThe final analysis incorporated a total of thirteen studies, which were published between 2019 and 2023, encompassing a participant cohort of 184,179 individuals. The SHR exhibited a significant association with the risk of various adverse outcomes. Specifically, a higher SHR was correlated with a 2.64-fold increased risk of 3-month poor functional outcomes (OR: 2.64, 95% CI 2.05–3.41, I2 = 52.3%, P &lt; 0.001), a 3.11-fold increased risk of 3-month mortality (OR: 3.11, 95% CI 2.10–4.59, I2 = 38.6%, P &lt; 0.001), a 2.80-fold increased risk of 1-year mortality (OR: 2.80, 95% CI 1.81–4.31, I2 = 88%, P &lt; 0.001), a 3.90-fold increased risk of intracerebral hemorrhage (ICH) and 4.57-fold increased risk of symptomatic ICH (sICH) (ICH-OR: 3.90, 95% CI 1.52–10.02, I2 = 84.3%, P = 0.005; sICH-OR: 4.57, 95% CI 2.05–10.10, I2 = 47.3%, P &lt; 0.001), a 1.73-fold increased risk of neurological deficits (OR: 1.73, 95 CI 1.44–2.08, I2 = 0%, P &lt; 0.001), and a 2.84-fold increased risk of stroke recurrence (OR: 2.84, 95 CI 1.48–5.45, I2 = 50.3%, P = 0.002). It is noteworthy that, except for hemorrhagic transformation (HT) and stroke recurrence, the remaining adverse outcomes exhibited a “J-shaped” non-linear dose-response relationship.ConclusionIn summary, our findings collectively suggest that increased exposure to elevated SHR is robustly linked to a heightened risk of adverse outcomes and mortality in individuals with AIS, exhibiting a non-linear dose-response relationship. These results underscore the significance of SHR as a predictive factor for stroke prognosis. Therefore, further investigations are warranted to explore the role of SHR in relation to adverse outcomes in stroke patients from diverse ethnic populations. Furthermore, there is a need to explore the potential benefits of stress hyperglycemia control in alleviating the physical health burdens associated with AIS. Maintaining a lower SHR level may potentially reduce the risk of adverse stroke outcomes.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42023424852