Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic 18FFET PET radiomics

PURPOSE To evaluate radiomic features extracted from standard static images (20-40~min p.i.), early summation images (5-15~min p.i.), and dynamic 18FFET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma. METHODS A total of 159 patients (median age 60.2~years, range 19-82~years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic 18FFET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20-40~min summation images; TBR20-40), early static (5-15~min summation images; TBR5-15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort. RESULTS The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95{\%} confidence interval 0.71-0.92) and sensitivity of 92.1{\%} in the independent testing cohort. Weak predictive capability was obtained in the TBR5-15 model, with an AUC of 0.61 (95{\%} CI 0.42-0.80) in the testing cohort, while no predictive power was observed in the TBR20-40 model. CONCLUSIONS Radiomics based on TTP images extracted from dynamic 18FFET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively

Prediction of non-genotoxic carcinogenicity based on genetic profiles of short term exposure assays

Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.Fil: Perez, Luis Orlando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico para el Estudio de los Ecosistemas Continentales; ArgentinaFil: González José, Rolando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico para el Estudio de los Ecosistemas Continentales; ArgentinaFil: Peral Garcia, Pilar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico CONICET- La Plata. Instituto de Genética Veterinaria ; Argentin

PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins

This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).Postprin

Regression Trees and Random forest based feature selection for malaria risk exposure prediction

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshold of variables importance is accessed using the quadratic distance of variables importance while the optimal subset of selected variables is used to perform predictions. Finally the results revealed to be qualitatively better, at the selection, the prediction , and the CPU time point of view than those obtained by GLM-Lasso method

Prediction of delayed graft function after kidney transplantation : comparison between logistic regression and machine learning methods

Background: Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF. Methods: 497 kidney transplantations from deceased donors at the Ghent University Hospital between 2005 and 2011 are included. A feature elimination procedure is applied to determine the optimal number of features, resulting in 20 selected parameters (24 parameters after conversion to indicator parameters) out of 55 retrospectively collected parameters. Subsequently, 9 distinct types of predictive models are fitted using the reduced data set: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs; using linear, radial basis function and polynomial kernels), decision tree (DT), random forest (RF), and stochastic gradient boosting (SGB). Performance of the models is assessed by computing sensitivity, positive predictive values and area under the receiver operating characteristic curve (AUROC) after 10-fold stratified cross-validation. AUROCs of the models are pairwise compared using Wilcoxon signed-rank test. Results: The observed incidence of DGF is 12.5 %. DT is not able to discriminate between recipients with and without DGF (AUROC of 52.5 %) and is inferior to the other methods. SGB, RF and polynomial SVM are mainly able to identify recipients without DGF (AUROC of 77.2, 73.9 and 79.8 %, respectively) and only outperform DT. LDA, QDA, radial SVM and LR also have the ability to identify recipients with DGF, resulting in higher discriminative capacity (AUROC of 82.2, 79.6, 83.3 and 81.7 %, respectively), which outperforms DT and RF. Linear SVM has the highest discriminative capacity (AUROC of 84.3 %), outperforming each method, except for radial SVM, polynomial SVM and LDA. However, it is the only method superior to LR. Conclusions: The discriminative capacities of LDA, linear SVM, radial SVM and LR are the only ones above 80 %. None of the pairwise AUROC comparisons between these models is statistically significant, except linear SVM outperforming LR. Additionally, the sensitivity of linear SVM to identify recipients with DGF is amongst the three highest of all models. Due to both reasons, the authors believe that linear SVM is most appropriate to predict DGF

Usefulness of gene expression profiling of bronchoalveolar lavage cells in acute lung allograft rejection.

BackgroundChronic lung allograft dysfunction (CLAD) is the main limitation to long-term survival after lung transplantation. Because effective therapies are lacking, early identification and mitigation of risk factors is a pragmatic approach to improve outcomes. Acute cellular rejection (ACR) is the most pervasive risk factor for CLAD, but diagnosis requires transbronchial biopsy, which carries risks. We hypothesized that gene expression in the bronchoalveolar lavage (BAL) cell pellet (CP) could replace biopsy and inform on mechanisms of CLAD.MethodsWe performed RNA sequencing on BAL CPs from 219 lung transplant recipients with A-grade ACR (n = 61), lymphocytic bronchiolitis (n = 58), infection (n = 41), or no rejection/infection (n = 59). Differential gene expression was based on absolute fold difference >2.0 and Benjamini-adjusted p-value ≤0.05. We used the Database for Annotation, Visualization and Integrated Discovery Bioinformatics Resource for pathway analyses. For classifier modeling, samples were randomly split into training (n = 154) and testing sets (n = 65). A logistic regression model using recursive feature elimination and 5-fold cross-validation was trained to optimize area under the curve (AUC).ResultsDifferential gene expression identified 72 genes. Enriched pathways included T-cell receptor signaling, natural killer cell-mediated cytotoxicity, and cytokine-cytokine receptor interaction. A 4-gene model (AUC = 0.72) and classification threshold defined in the training set exhibited fair performance in the testing set; accuracy was 76%, specificity 82%, and sensitivity 60%. In addition, classification as ACR was associated with worse CLAD-free survival (hazard ratio = 2.42; 95% confidence interval = 1.29-4.53).ConclusionsBAL CP gene expression during ACR is enriched for immune response pathways and shows promise as a diagnostic tool for ACR, especially ACR that is a precursor of CLAD