A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population
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PROVIDER: S-EPMC10907009 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
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Project description:Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.
| S-EPMC8664243 | biostudies-literature
Project description:Over recent years, the state-of-the-art lasso and adaptive lasso have aquired remarkable consideration. Unlike the lasso technique, adaptive lasso welcomes the variables’ effects in penalty meanwhile specifying adaptive weights to penalize coefficients in a different manner. However, if the initial values presumed for the coefficients are less than one, the corresponding weights would be relatively large, leading to an increase in bias. To dominate such an impediment, a new class of weighted lasso will be introduced that employs all aspects of data. That is to say, signs and magnitudes of the initial coefficients will be taken into account simultaneously for proposing appropriate weights. To allocate a particular form to the suggested penalty, the new method will be nominated as ‘lqsso’, standing for the least quantile shrinkage and selection operator. In this paper, we demonstate that lqsso encompasses the oracle properties under certain mild conditions and delineate an efficient algorithm for the computation purpose. Simulation studies reveal the predominance of our proposed methodology when compared with other lasso methods from various aspects, particularly in ultra high-dimensional condition. Application of the proposed method is further underlined with real-world problem based on the rat eye dataset.
| S-EPMC9934385 | biostudies-literature
Project description:BackgroundThis study aimed to find ferroptosis-related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.MethodsWe downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis-related genes were screened by univariate regression analysis, machine-learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis-related gene-based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival-associated ferroptosis-related genes.ResultsSeventy genes were identified as survival-associated ferroptosis-related genes. The prognostic model was constructed with 17 ferroptosis-related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1-, 3-, and 5- year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.ConclusionWe constructed a prognosis model of ACC based on ferroptosis-related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.
| S-EPMC9169198 | biostudies-literature
Project description:Measurement of serum biomarkers by multiplex assays may be more variable as compared to single biomarker assays. Measurement error in these data may bias parameter estimates in regression analysis, which could mask true associations of serum biomarkers with an outcome. The Least Absolute Shrinkage and Selection Operator (LASSO) can be used for variable selection in these high-dimensional data. Furthermore, when the distribution of measurement error is assumed to be known or estimated with replication data, a simple measurement error correction method can be applied to the LASSO method. However, in practice the distribution of the measurement error is unknown and is expensive to estimate through replication both in monetary cost and need for greater amount of sample which is often limited in quantity. We adapt an existing bias correction approach by estimating the measurement error using validation data in which a subset of serum biomarkers are re-measured on a random subset of the study sample. We evaluate this method using simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). We show that the bias in parameter estimation is reduced and variable selection is improved.
| S-EPMC7449511 | biostudies-literature
Project description:Objective: Dilated cardiomyopathy (DCM) is a heart disease with high mortality characterized by progressive cardiac dilation and myocardial contractility reduction. The molecular signature of dilated cardiomyopathy remains to be defined. Hence, seeking potential biomarkers and therapeutic of DCM is urgent and necessary. Methods: In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate four eligible DCM microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between dilated cardiomyopathy and non-heart failure. Moreover, LASSO analysis was carried out to clarify the diagnostic and DCM clinical features of these genes and identify dilated cardiomyopathy derived diagnostic signatures (DCMDDS). Results: A total of 117 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in the regulation of inflammatory response, the humoral immune response, the regulation of blood pressure and collagen-containing extracellular matrix. In addition, KEGG analysis revealed that DEGs were mainly enriched in diverse infected signaling pathways. Moreover, Gene set enrichment analysis revealed that immune and inflammatory biological processes such as adaptive immune response, cellular response to interferon and cardiac muscle contraction, dilated cardiomyopathy are significantly enriched in DCM. Moreover, Least absolute shrinkage and selection operator (LASSO) analyses of the 18 DCM-related genes developed a 7-gene signature predictive of DCM. This signature included ANKRD1, COL1A1, MYH6, PERELP, PRKACA, CDKN1A, and OMD. Interestingly, five of these seven genes have a correlation with left ventricular ejection fraction (LVEF) in DCM patients. Conclusion: Our present study demonstrated that the signatures could be robust tools for predicting DCM in clinical practice. And may also be potential treatment targets for clinical implication in the future.
| S-EPMC8713643 | biostudies-literature
Project description:BackgroundBecause multiple loci control complex diseases, there is great interest in testing markers simultaneously instead of one by one. In this paper, we applied two model selection algorithms: the stochastic search variable selection (SSVS) and the least absolute shrinkage and selection operator (LASSO) to two quantitative phenotypes related to rheumatoid arthritis (RA).ResultsThe Genetic Analysis Workshop 16 data includes 2,062 unrelated individuals and 545,080 single-nucleotide polymorphism markers from the Illumina 550 k chip. We performed our analyses on the cases as the quantitative phenotype data was not provided for the controls. The performance of the two algorithms was compared. Using sure independence screening as the prescreening procedure, both SSVS and LASSO give small models. No markers are identified in the human leukocyte antigen region of chromosome 6 that was shown to be associated with RA. SSVS and LASSO identify seven common loci, and some of them are on genes LRRC8D, LRP1B, and COLEC12. These genes have not been reported to be associated with RA. LASSO also identified a common locus on gene KTCD21 for the two phenotypes (marker rs230662 and rs483731, respectively).ConclusionSSVS outperforms LASSO in simulation studies. Both SSVS and LASSO give small models on the RA data, however this depends on model parameters. We also demonstrate the ability of both LASSO and SSVS to handle more markers than the number of samples.
| S-EPMC2795918 | biostudies-literature
Project description:There are proposals that extend the classical generalized additive models (GAMs) to accommodate high-dimensional data ( p≫n$$ p\gg n $$ ) using group sparse regularization. However, the sparse regularization may induce excess shrinkage when estimating smooth functions, damaging predictive performance. Moreover, most of these GAMs consider an "all-in-all-out" approach for functional selection, rendering them difficult to answer if nonlinear effects are necessary. While some Bayesian models can address these shortcomings, using Markov chain Monte Carlo algorithms for model fitting creates a new challenge, scalability. Hence, we propose Bayesian hierarchical generalized additive models as a solution: we consider the smoothing penalty for proper shrinkage of curve interpolation via reparameterization. A novel two-part spike-and-slab LASSO prior for smooth functions is developed to address the sparsity of signals while providing extra flexibility to select the linear or nonlinear components of smooth functions. A scalable and deterministic algorithm, EM-Coordinate Descent, is implemented in an open-source R package BHAM. Simulation studies and metabolomics data analyses demonstrate improved predictive and computational performance against state-of-the-art models. Functional selection performance suggests trade-offs exist regarding the effect hierarchy assumption.
| S-EPMC10390213 | biostudies-literature
Project description: Not available
| S-EPMC10928614 | biostudies-literature
Project description:BackgroundHyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease.MethodsPatients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated.ResultsA total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%.ConclusionsWe developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.
| S-EPMC10976486 | biostudies-literature
Project description:Statistical analysis to evaluate mechanistic pathways can be limited by non-causal associations as well as co-linearity of high-dimensional data. Here, we present a protocol evaluating statistical associations between multiple exposure variables (sociodemographic and behavioral), immune biomarkers, and HIV acquisition. We describe steps for study setup, combining Least Absolute Shrinkage and Selective Operator with the standard regression approach, and building nested models. This approach can determine to what extent associations between risks for exposure contributes to HIV acquisition with or without associated changes in immune activation. For complete details on the use and execution of this protocol, please refer to Bender Ignacio et al.1.
| S-EPMC10568409 | biostudies-literature