scholarly journals Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Mei-Ling Hou ◽  
Shu-Lin Wang ◽  
Xue-Ling Li ◽  
Ying-Ke Lei
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Rough Set ◽  
Gene Selection ◽  
Cancer Biomarkers ◽  
Cancer Classification ◽  
Tumor Classification ◽  
Cancer Type ◽  
Neighborhood Rough Set

Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.

scholarly journals Development and Validation of a Diagnostic 35-Gene Expression Profile Test for Ambiguous or Difficult-to-Diagnose Suspicious Pigmented Skin Lesions

2020 ◽  
Vol 4 (6) ◽  
pp. 506-522
Author(s):  
Sarah Estrada ◽  
Jeffrey Shackelton ◽  
Nathan Cleaver ◽  
Natalie Depcik-Smith ◽  
Clay Cockerell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Predictive Value ◽  
Gene Selection ◽  
Skin Lesions ◽  
Neural Network Modeling ◽  
Intermediate Risk ◽  
Pigmented Lesions ◽  
Development And Validation

Purpose: A clinical hurdle for dermatopathology is the accurate diagnosis of melanocytic neoplasms. While histopathologic assessment is frequently sufficient, high rates of diagnostic discordance are reported. The development and validation of a 35-gene expression profile (35-GEP) test that accurately differentiates benign and malignant pigmented lesions is described. Methods: Lesion samples were reviewed by at least three independent dermatopathologists and included in the study if 2/3 or 3/3 diagnoses were concordant. Diagnostic utility of 76 genes was assessed with quantitative RT-PCR; neural network modeling and cross-validation were utilized for diagnostic gene selection using 200 benign nevi and 216 melanomas for training. To reflect the complex biology of melanocytic neoplasia, the 35-GEP test was developed to include an intermediate-risk zone. Results: Validation of the 35-GEP was performed in an independent set of 273 benign and 230 malignant lesions. The test demonstrated 99.1% sensitivity, 94.3% specificity, 93.6% positive predictive value and 99.2% negative predictive value. 96.4% of cases received a differential result and 3.6% had intermediate-risk. Conclusions: The 35-GEP test was developed to refine diagnoses of melanocytic neoplasms by providing clinicians with an objective tool. A test with these accuracy metrics could alleviate uncertainty in difficult-to-diagnose lesions leading to decreased unnecessary procedures while appropriately identifying at-risk patients.

A Novel Gene Selection Algorithm based on Sparse Representation and Minimum-redundancy Maximum-relevancy of Maximum Compatibility Center

2019 ◽  
Vol 16 (5) ◽  
pp. 374-382 ◽  
Author(s):  
Min Chen ◽  
Yi Zhang ◽  
Zejun Li ◽  
Ang Li ◽  
Wenhua Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sparse Representation ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Classification Accuracy ◽  
Gene Selection ◽  
Expression Profiles ◽  
Biological Significance ◽  
Complex Diseases ◽  
Small Time

Background: Tumor classification is important for accurate diagnosis and personalized treatment and has recently received great attention. Analysis of gene expression profile has shown relevant biological significance and thus has become a research hotspot and a new challenge for bio-data mining. In the research methods, some algorithms can identify few genes but with great time complexity, some algorithms can get small time complex methods but with unsatisfactory classification accuracy, this article proposed a new extraction method for gene expression profile. Methods: In this paper, we propose a classification method for tumor subtypes based on the Minimum- Redundancy Maximum-Relevancy (MRMR) of maximum compatibility center. First, we performed a fuzzy clustering of gene expression profiles based on the compatibility relation. Next, we used the sparse representation coefficient to assess the importance of the gene for the category, extracted the top-ranked genes, and removed the uncorrelated genes. Finally, the MRMR search strategy was used to select the characteristic gene, reject the redundant gene, and obtain the final subset of characteristic genes. Results: Our method and four others were tested on four different datasets to verify its effectiveness. Results show that the classification accuracy and standard deviation of our method are better than those of other methods. Conclusion: Our proposed method is robust, adaptable, and superior in classification. This method can help us discover the susceptibility genes associated with complex diseases and understand the interaction between these genes. Our technique provides a new way of thinking and is important to understand the pathogenesis of complex diseases and prevent diseases, diagnosis and treatment.

scholarly journals Predicting chemotherapy response using a variational autoencoder approach

2021 ◽  
Author(s):  
Qi Wei ◽  
Stephen A. Ramsey
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Supervised Classification ◽  
Supplementary Information ◽  
Chemotherapy Response ◽  
Cancer Type ◽  
Response To Chemotherapy ◽  
Variational Autoencoder ◽  
Cancer Types

AbstractMotivationMultiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon adenocarcinoma, pancreatic adenocarcinoma, bladder carcinoma, sarcoma, and breast invasive carcinoma.ResultsWe found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor’s gene expression profile leads to better area under the receiver operating characteristic curve (AUROC) classification performance than either the original gene expression profile or the PCA principal components of the gene expression profile, in four out of five cancer types that we tested.Availabilitygithub.com/ATHED/VAE_for_chemotherapy_drug_response_predictionContactramseyst@oregonstate.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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