Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression

Algamal, Zakariya Yahya; Alhamzawi, Rahim; Ali, Haithem Taha Mohammad

doi:10.1016/j.compbiomed.2018.04.018

Cited by 29 publications

(16 citation statements)

References 45 publications

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“…The second approach used only those traits selected by the LASSO selection procedure. For both approaches, we used the DNN implemented in Google Tensor Flow package (Abadi et al 2016). We explored different type of optimizers and activation algorithms, and settled on the AdamOptimizer and elu activation algorithm.…”

Section: Variant Selection Model Testing and Validationmentioning

confidence: 99%

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits

Chen

Mize

et al. 2018

J Neuroimmune Pharmacol

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Schizophrenia is genetically heterogeneous and comorbid with many conditions. In this study, we explored polygenic scores (PGSs) from genetically related conditions and traits to predict schizophrenia diagnosis using both logistic regression and deep neural network (DNN) models. We used the combined Molecular Genetics of Schizophrenia and Swedish Schizophrenia Case Control Study (MGS+SSCCS) data for training and testing the models, and used the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) data as independent validation. We screened 28 conditions and traits comorbid with schizophrenia to identify traits as potential predictors and used LASSO regression to select predictors for model construction. We investigated how PGS calculation influenced model performance. We found that the inclusion of comorbid traits improved model performance and PGSs calculated from two traits were more generalizable in independent validation. With a DNN model using 19 PGS predictors, we accomplished a prediction accuracy of 0.813 and an AUC of 0.905 in the MGS+SSCCS data. When this model was validated with the CATIE data, it achieved an accuracy of 0.721 and AUC of 0.747. Our results indicate that PGSs alone may not be sufficient to predict schizophrenia accurately and the inclusion of behavioral and clinical data may be necessary for more accurate prediction model.

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Section: Variant Selection Model Testing and Validationmentioning

confidence: 99%

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits

Chen

Mize

et al. 2018

J Neuroimmune Pharmacol

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show abstract

“…This indicated that LASSO can effectively choose some variables by reducing the coefficient of others to exactly zero. Thus, for features selection in different scientific area LASSO can be used effectively [24], [25]. Method of cross validation is normally employed for selecting the value of for identifying the best value of that has the smallest measure of accuracy (mis classification error) [26].…”

Section: Penalized Logistic Regressionmentioning

confidence: 99%

Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

2021

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<p class="0abstract">Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other. As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both and simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity.</p>

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“…Directly processing original high dimensional microarray data not only degenerates the classification performance but also brings extra computation burden of hardware. Therefore, it is necessary to select a subset of discriminative genes from high-dimensional microarray data to serve subsequent tasks [16][17][18][19][20][21][22][23][24][25]. If we treat each gene as a feature dimension in microarray data, gene selection is similar to the feature selection task in machine learning and data mining community [26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Gene Correlation Guided Gene Selection for Microarray Data Classification

Yang

Zhu

2021

BioMed Research International

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The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

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Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression

Cited by 29 publications

References 45 publications

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits

Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits

Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

Gene Correlation Guided Gene Selection for Microarray Data Classification

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