Gene regulatory networks reveal how genes work together to carry out their biological functions. Reconstructions of gene networks from gene expression data greatly facilitate our understanding of ...underlying biological mechanisms and provide new opportunities for biomarker and drug discoveries. In gene networks, a gene that has many interactions with other genes is called a hub gene, which usually plays an essential role in gene regulation and biological processes. In this study, we developed a method for reconstructing gene networks using a partial correlation-based approach that incorporates prior information about hub genes. Through simulation studies and two real-data examples, we compare the performance in estimating the network structures between the existing methods and the proposed method.
In simulation studies, we show that the proposed strategy reduces errors in estimating network structures compared to the existing methods. When applied to Escherichia coli, the regulation network constructed by our proposed ESPACE method is more consistent with current biological knowledge than the SPACE method. Furthermore, application of the proposed method in lung cancer has identified hub genes whose mRNA expression predicts cancer progress and patient response to treatment.
We have demonstrated that incorporating hub gene information in estimating network structures can improve the performance of the existing methods.
This work is motivated by the recent worldwide pandemic of the novel coronavirus disease (COVID-19). When an epidemiological disease is prevalent, estimating the case fatality rate, the proportion of ...deaths out of the total cases, accurately and quickly is important as the case fatality rate is one of the crucial indicators of the risk of a disease. In this work, we propose an alternative estimator of the case fatality rate that provides more accurate estimate during an outbreak by reducing the downward bias (underestimation) of the naive CFR, the proportion of deaths out of confirmed cases at each time point, which is the most commonly used estimator due to the simplicity. The proposed estimator is designed to achieve the availability of real-time update by using the commonly reported quantities, the numbers of confirmed, cured, deceased cases, in the computation. To enhance the accuracy, the proposed estimator adapts a stratification, which allows the estimator to use information from heterogeneous strata separately. By the COVID-19 cases of China, South Korea and the United States, we numerically show the proposed stratification-based estimator plays a role of providing an early warning about the severity of a epidemiological disease that estimates the final case fatality rate accurately and shows faster convergence to the final case fatality rate.
Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were ...proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.
Condition-number-regularized covariance estimation Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean ...
Journal of the Royal Statistical Society. Series B, Statistical methodology,
June 2013, Letnik:
75, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Estimation of high dimensional covariance matrices is known to be a difficult problem, has many applications and is of current interest to the larger statistics community. In many applications ...including the so-called 'large p, small n' setting, the estimate of the covariance matrix is required to be not only invertible but also well conditioned. Although many regularization schemes attempt to do this, none of them address the ill conditioning problem directly. We propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumptions on either the covariance matrix or its inverse are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision theoretic comparisons and in the financial portfolio optimization setting. The approach proposed has desirable properties and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.
To perform a systematic review and meta-analysis on the efficacy and safety of intravenous (IVIg) and subcutaneous (SCIg) immunoglobulin (Ig) therapy in the treatment of idiopathic inflammatory ...myopathy (IIM) and juvenile dermatomyositis (JDM).
PubMed, Embase and SCOPUS were searched to identify studies on Ig therapy in patients with IIM and/or JDM (2010−2020). Outcome measures were complete response (CR) or partial response (PR) in terms of muscle power and extramuscular disease activity measures on the International Myositis Assessment and Clinical Studies Group (IMACS) core set domains.
Twenty-nine studies were included (n = 576, 544 IIM, 32 JDM). Muscle power PR with pooled Ig therapy was 88.5% (95% confidence interval (CI): 80.6–93.5, n = 499) and PR with SCIg treatment was 96.61% (95% CI: 87.43–99.15, n = 59). Pooled PR with first-line use of IVIg was 77.07% (95% CI: 61.25–92.89, n = 80). Overall, mean time to response was 2.9 months (95% CI: 1.9–4.1). Relapse was seen in 22.76% (95% CI: 14.9–33). Studies on cutaneous disease activity and dysphagia showed significant treatment responses. Glucocorticoid and immunosuppressant sparing effect was seen in 40.9% (95% CI: 20–61.7) and 42.2% (95% CI: 20.4–64.1) respectively. Ig therapy was generally safe with low risk of infection (1.37%, 95% CI: 0.1–2.6).
Add-on Ig therapy improves muscle strength in patients with refractory IIM, but evidence on Ig therapy in new-onset disease and extramuscular disease activity is uncertain.
•Add-on Ig therapy improves muscle strength in patients with refractory IIM.•Add-on Ig therapy improves cutaneous manifestation in patients with refractory DM.•Ig therapy improves extra-muscular disease activity and has steroid-sparing effect.•First-line Ig therapy improves muscular and extra-muscular disease activity in IIM.•Ig therapy appears generally safe with a relatively low rate of infectious complications.
Abstract
Objectives
We explored efficacy and safety of IVIg as first-line treatment in patients with an idiopathic inflammatory myopathy.
Methods
In this investigator-initiated phase 2 open-label ...study, we included 20 adults with a newly diagnosed, biopsy-proven idiopathic inflammatory myopathy, and a disease duration of less than 9 months. Patients with IBM and prior use of immunosuppressants were excluded. The standard treatment regimen consisted of IVIg (Privigen) monotherapy for 9 weeks: a loading dose (2 g/kg body weight) and two subsequent maintenance doses (1 g/kg body weight) with a 3-week interval. The primary outcome was the number of patients with at least moderate improvement on the 2016 ACR/EULAR Total Improvement Score. Secondary outcomes included time to improvement, the number of patients requiring rescue medication and serious adverse events.
Results
We included patients with DM (n = 9), immune-mediated necrotizing myopathy (n = 6), non-specific myositis/overlap myositis (n = 4) and anti-synthetase syndrome (n = 1). One patient was excluded from analyses because of minimal weakness resulting in a ceiling effect. Eight patients (8/19 = 42.0%; Clopper–Pearson 95% CI: 19.6, 64.6) had at least moderate improvement by 9 weeks. Of these, six reached improvement by 3 weeks. Seven patients required rescue medication due to insufficient efficacy and prematurely ended the study. Three serious adverse events occurred, of which one was pulmonary embolism.
Conclusion
First-line IVIg monotherapy led to at least moderate improvement in nearly half of patients with a fast clinical response in the majority of responders.
Trial registration
Netherlands Trial Register identifier, NTR6160.
Lipid mediators are crucial for the pathogenesis of rheumatoid arthritis (RA); however, global analyses have not been undertaken to systematically define the lipidome underlying the dynamics of ...disease evolution, activation, and resolution. Here, we performed untargeted lipidomics analysis of synovial fluid and serum from RA patients at different disease activities and clinical phases (preclinical phase to active phase to sustained remission). We found that the lipidome profile in RA joint fluid was severely perturbed and that this correlated with the extent of inflammation and severity of synovitis on ultrasonography. The serum lipidome profile of active RA, albeit less prominent than the synovial lipidome, was also distinguishable from that of RA in the sustained remission phase and from that of noninflammatory osteoarthritis. Of note, the serum lipidome profile at the preclinical phase of RA closely mimicked that of active RA. Specifically, alterations in a set of lysophosphatidylcholine, phosphatidylcholine, ether-linked phosphatidylethanolamine, and sphingomyelin subclasses correlated with RA activity, reflecting treatment responses to anti-rheumatic drugs when monitored serially. Collectively, these results suggest that analysis of lipidome profiles is useful for identifying biomarker candidates that predict the evolution of preclinical to definitive RA and could facilitate the assessment of disease activity and treatment outcomes.
Infestation by the biotrophic pathogen Gymnosporangium asiaticum can be devastating for plant of the family Rosaceae. However, the phytopathology of this process has not been thoroughly elucidated. ...Using a metabolomics approach, we discovered the intrinsic activities that induce disease symptoms after fungal invasion in terms of microbe-induced metabolic responses. Through metabolic pathway enrichment and mapping, we found that the host altered its metabolite levels, resulting in accumulation of tetrose and pentose sugar alcohols, in response to this fungus. We then used a multiple linear regression model to evaluate the effect of the interaction between this abnormal accumulation of sugar alcohol and the group variable (control/parasitism). The results revealed that this accumulation resulted in deficiency in the supply of specific sugars, which led to a lack of amino sugar and nucleotide sugar metabolism. Halting this metabolism could hamper pivotal functions in the plant host, including cell wall synthesis and lesion repair. In conclusion, our findings indicate that altered metabolic responses that occur during fungal parasitism can cause deficiency in substrates in pivotal pathways and thereby trigger pathological symptoms.
The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform ...transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in
infection and inflammatory processes. The overexpression of
and
was associated with poor disease-free survival while the down-regulation of
,
, and
was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.
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