Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.
Transforming growth factor beta (TGF-beta) is an example for a large and still-growing family of growth factors. TGF-beta1 is known to act both as a tumour suppressor and as a stimulator of tumour progression. This study examines the relationship amongst putative enhancer, promoter, 5'-untranslated-region (UTR) and exon-1 polymorphisms of the TGF-beta1 gene (region I from -1881 to -1613; region II from -1410 to -1123, and region III from -55 to +176, as per human genome organisation (HUGO) nomenclature) in 26 breast cancer patients and 97 healthy control subjects. The germline and somatic status of the four known polymorphisms was ascertained, and a significant difference was observed for the germline C/T and T/T genotype distribution between patients and controls in comparison to C/C genotypes at position -1349 (chi2 = 6.193; P = 0.009). In addition to the somatic variations observed for some of the regions studied, in 10/26 (38%) sporadic breast cancer cases, a novel somatic mutation in codon 47 of exon 1 (GenBank accession number AY059373) was also detected in tumour samples. The risk of cancer was found to be significant (OR = 4.525) for the -1349 C/T and T/T genotype background, suggesting that this genetic background may act as a risk factor for sporadic breast cancer.
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.