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Objective: This study aims to explore the use of machine learning algorithms for predicting disease classification. Methods: An integrated algorithm (KPLSKELM) was proposed in this study. The algorithm employed kernel principal component analysis to transform the original data into a high-dimensional feature space, thereby enhancing its linear separability. It used the sparrow search algorithm (SSA) to optimize the weight matrix and parameters of the kernel extreme learning machine (KELM). The algorithm incorporated a Gaussian perturbation search mechanism to refine the population initialization strategy so as to mitigate the issues of poor convergence rate and susceptibility to local optima in the later SSA iterations. Lévy flight perturbations were introduced during the foraging search process of the sparrow population to guide the population in moving appropriate step sizes, thereby increasing the diversity of the spatial search. The proposed method was experimentally validated using a binary classification breast cancer dataset collected by Dr. William H. Wolberg from a Wisconsin hospital in the United States and a multiclass classification dataset of electrocardiographic recordings during childbirth. Multiple metrics were adopted to evaluate the classification performance. Results: The accuracy and F1_score of the KELM model remained relatively low across different percentages of the training set, although a recall of 1.0000 was consistently achieved. Both the SSA-improved KELM and the Lévy-improved SSA-optimized KELM algorithms exhibited better performance in terms of the comprehensive metric F1_score and improved with the increase in the percentage of the training set. The KPLSKELM model outperformed others in all metrics, with accuracy, precision, recall, and F1_score approaching or reaching the highest levels when using 90% of the training set. Conclusions: The proposed method demonstrated excellent performance in various disease prediction tasks, holding high practical application value. It provided a reference for further assisting clinicians in making more precise treatment decisions.

Hantavirus outbreaks in the American Southwest are hypothesized to be driven by episodic seasonal events of high precipitation, promoting rapid increases in virus-reservoir rodent species that then move across the landscape from high quality montane forested habitats (refugia), eventually over-running human residences and increasing disease risk. In this study, the velocities of rodents and virus diffusion wave propagation and retraction were documented and quantified in the sky-islands of northern New Mexico and related to rodent-virus relationships in refugia versus nonrefugia habitats. Deer mouse (Peromyscus maniculatus) refugia populations exhibited higher Sin Nombre Virus (SNV) infection prevalence than nonrefugia populations. The velocity of propagating diffusion waves of Peromyscus from montane to lower grassland habitats was measured at $24.6\pm 5.6$ m/day (SE), with wave retraction velocities of $28\pm 8.4$ m/day. SNV infection diffusion wave propagation velocity within a deer mouse population averaged $27.5\pm 7.8$ m/day, with a faster retraction wave velocity of $161.5\pm 80.7$ m/day. A spatio-temporal analysis of human Hantavirus Pulmonary Syndrome (HPS) cases during the initial 1993 epidemic revealed a positive linear relationship between the time during the epidemic and the distance of human cases from the nearest deer mouse refugium, with a landscape diffusion wave velocity of $19.6\pm 1.0$ m/day ($r^2=0.96$). These consistent diffusion propagation wave velocity results support the traveling wave component of the HPS outbreak theory and can provide information on space–time constraints for future outbreak forecasts.

In this paper, we have investigated women’s malignant disease, cervical cancer, by constructing the compartmental model. An extended fractal–fractional model is used to study the disease dynamics. The points of equilibria are computed analytically and verified by numerical simulations. The key role of $R_0$ in describing the stability of the model is presented. The sensitivity analysis of $R_0$ for deciding the role of certain parameters altering the disease dynamics is carried out. The numerical simulations of the proposed numerical technique are demonstrated to test the claimed facts.

Physiological status and pathological changes in an individual can be captured by metabolic state that reflects the influence of both genetic variants and environmental factors such as diet, lifestyle and gut microbiome. The totality of environmental exposure throughout lifetime – i.e., exposome – is difficult to measure with current technologies. However, targeted measurement of exogenous chemicals and untargeted profiling of endogenous metabolites have been widely used to discover biomarkers of pathophysiologic changes and to understand functional impacts of genetic variants. To investigate the coverage of chemical space and interindividual variation related to demographic and pathological conditions, we profiled 169 plasma samples using an untargeted metabolomics platform. On average, 1,009 metabolites were quantified in each individual (range 906 – 1,038) out of 1,244 total chemical compounds detected in our cohort. Of note, age was positively correlated with the total number of detected metabolites in both males and females. Using the robust Q_n estimator, we found metabolite outliers in each sample (mean 22, range from 7 to 86). A total of 50 metabolites were outliers in a patient with phenylketonuria including the ones known for phenylalanine pathway suggesting multiple metabolic pathways perturbed in this patient. The largest number of outliers (N=86) was found in a 5-year-old boy with alpha-1-antitrypsin deficiency who were waiting for liver transplantation due to cirrhosis. Xenobiotics including drugs, diets and environmental chemicals were significantly correlated with diverse endogenous metabolites and the use of antibiotics significantly changed gut microbial products detected in host circulation. Several challenges such as annotation of features, reference range and variance for each feature per age group and gender, and population scale reference datasets need to be addressed; however, untargeted metabolomics could be immediately deployed as a biomarker discovery platform and to evaluate the impact of genomic variants and exposures on metabolic pathways for some diseases.

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.