scholarly journals Pheo-Type: A Diagnostic Gene-expression Assay for the Classification of Pheochromocytoma and Paraganglioma

2016 ◽  
Vol 101 (3) ◽  
pp. 1034-1043 ◽  
Author(s):  
Aidan Flynn ◽  
Trisha Dwight ◽  
Jessica Harris ◽  
Diana Benn ◽  
Li Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Classification Accuracy ◽  
Microarray Dataset ◽  
Support Vector ◽  
Gene Expression Assay ◽  
Expression Platform ◽  
Gene Expression Platform ◽  
Surveillance Programs ◽  
Sporadic Cases ◽  
Type Gene

Abstract Context: Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers. Objective: This study aimed to develop a diagnostic and research tool (Pheo-type) capable of classifying PPGL tumors into gene-expression subtypes that could be used to guide and interpret genetic testing, determine surveillance programs, and aid in elucidation of PPGL biology. Design: A compendium of published microarray data representing 205 PPGL tumors was used for the selection of subtype-specific genes that were then translated to the Nanostring gene-expression platform. A support vector machine was trained on the microarray dataset and then tested on an independent Nanostring dataset representing 38 familial and sporadic cases of PPGL of known genotype (RET, NF1, TMEM127, MAX, HRAS, VHL, and SDHx). Different classifier models involving between three and six subtypes were compared for their discrimination potential. Results: A gene set of 46 genes and six endogenous controls was selected representing six known PPGL subtypes; RTK1–3 (RET, NF1, TMEM127, and HRAS), MAX-like, VHL, and SDHx. Of 38 test cases, 34 (90%) were correctly predicted to six subtypes based on the known genotype to gene-expression subtype association. Removal of the RTK2 subtype from training, characterized by an admixture of tumor and normal adrenal cortex, improved the classification accuracy (35/38). Consolidation of RTK and pseudohypoxic PPGL subtypes to four- and then three-class architectures improved the classification accuracy for clinical application. Conclusions: The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.

scholarly journals bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays

2017 ◽  
Author(s):  
Kevin YX Wang ◽  
Alexander M Menzies ◽  
Ines P Silva ◽  
James S Wilmott ◽  
Yibing Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bias Correction ◽  
Web Application ◽  
Gene Annotation ◽  
Correction Method ◽  
Implicit Assumption ◽  
Exact Test ◽  
Gene Set ◽  
Expression Platform ◽  
Gene Expression Platform

AbstractMotivation: Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes are important tools in Gene Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene set selection bias induced during the construction of these platforms.Results: We propose bcGST, a bias-corrected Gene Set Test by introducing bias correction terms in the contingency table needed for calculating the Fisher’s Exact Test (FET). The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and TCGA cancer studies.Availability: The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/Contact:[email protected]

scholarly journals Application of a Scalable Plant Transient Gene Expression Platform for Malaria Vaccine Development

2015 ◽  
Vol 6 ◽  
Author(s):  
Holger Spiegel ◽  
Alexander Boes ◽  
Nadja Voepel ◽  
Veronique Beiss ◽  
Gueven Edgue ◽  
...  
Keyword(s):  
Gene Expression ◽  
Malaria Vaccine ◽  
Vaccine Development ◽  
Transient Gene Expression ◽  
Expression Platform ◽  
Gene Expression Platform

scholarly journals Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

2020 ◽  
Author(s):  
Mohamed Loey ◽  
Mohammed Wajeeh Jasim ◽  
Hazem M. EL-Bakry ◽  
Mohamed Hamed N. Taha ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Gene Expression ◽  
Classification Accuracy ◽  
Information Gain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Disease Diagnosis ◽  
Performance Measure ◽  
Support Vector ◽  
Svm Classifier ◽  
Cancer Type

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

scholarly journals Development of a gene expression–based prognostic signature for IDH wild-type glioblastoma

2020 ◽  
Vol 22 (12) ◽  
pp. 1742-1756 ◽  
Author(s):  
Radia M Johnson ◽  
Heidi S Phillips ◽  
Carlos Bais ◽  
Cameron W Brennan ◽  
Timothy F Cloughesy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Trial ◽  
Prognostic Value ◽  
Dna Methyltransferase ◽  
Methylation Status ◽  
Elastic Net ◽  
Wild Type ◽  
Prognostic Signature ◽  
Expression Platform ◽  
Gene Expression Platform

Abstract Background We aimed to develop a gene expression–based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations. Methods Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as “ATE”), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net–selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts. Results NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status. Conclusions The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

scholarly journals Breast and Colon Cancer Classification from Gene Expression Profiles Using Data Mining Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 408 ◽  
Author(s):  
Mohamed Loey Ramadan AbdElNabi ◽  
Mohammed Wajeeh Jasim ◽  
Hazem M. EL-Bakry ◽  
Mohamed Hamed N. Taha ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Gene Expression ◽  
Classification Accuracy ◽  
Information Gain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Disease Diagnosis ◽  
Performance Measure ◽  
Support Vector ◽  
Svm Classifier ◽  
Cancer Type

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (in the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection.

A HYBRID OF GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE FOR FEATURES SELECTION AND CLASSIFICATION OF GENE EXPRESSION MICROARRAY

2005 ◽  
Vol 05 (01) ◽  
pp. 91-107 ◽  
Author(s):  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
ROSLI MD ILLIAS
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Hybrid Genetic Algorithm ◽  
Support Vector ◽  
Small Subset ◽  
Features Selection ◽  
Gene Expression Microarray ◽  
Expression Microarray

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.

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