Dataset Information

Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.

SUBMITTER: Ji Z

PROVIDER: S-EPMC3976846 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.

Ji Zhiwei Z Wang Bing B

BioMed research international 20140317

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by ...[more]

PMID: 24745007

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Project description:BackgroundLung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identify features with the ability to distinguish between two data groups of interest, which is thus highly applicable for such studies.ResultsMicroarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells (60 samples from smokers with lung cancer and 69 from smokers without lung cancer) and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set, BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI (given the same number of genes and classification algorithms) showed better discriminative power than those from the original study. After pathway analysis on the selected genes by BMI, we have been able to correlate the selected genes with well-known cancer-related pathways.ConclusionsOur results show that BMI can be used to analyze microarray data and to find useful genes for classifying samples. Pathway analysis suggests that BMI is successful in identifying biomarker-quality cancer-related genes from the data.

Project description:Feature selection plays a crucial role in classification tasks as part of the data preprocessing process. Effective feature selection can improve the robustness and interpretability of learning algorithms, and accelerate model learning. However, traditional statistical methods for feature selection are no longer practical in the context of high-dimensional data due to the computationally complex. Ensemble learning, a prominent learning method in machine learning, has demonstrated exceptional performance, particularly in classification problems. To address the issue, we propose a three-stage feature selection algorithm framework for high-dimensional data based on ensemble learning (EFS-GINI). Firstly, highly linearly correlated features are eliminated using the Spearman coefficient. Then, a feature selector based on the F-test is employed for the first stage selection. For the second stage, four feature subsets are formed using mutual information (MI), ReliefF, SURF, and SURF* filters in parallel. The third stage involves feature selection using a combinator based on GINI coefficient. Finally, a soft voting approach is proposed to employ for classification, including decision tree, naive Bayes, support vector machine (SVM), k-nearest neighbors (KNN) and random forest classifiers. To demonstrate the effectiveness and efficiency of the proposed algorithm, eight high-dimensional datasets are used and five feature selection methods are employed to compare with our proposed algorithm. Experimental results show that our method effectively enhances the accuracy and speed of feature selection. Moreover, to explore the biological significance of the proposed algorithm, we apply it on the renal cell carcinoma dataset GSE40435 from the Gene Expression Omnibus database. Two feature genes, NOP2 and NSUN5, are selected by our proposed algorithm. They are directly involved in regulating m5c RNA modification, which reveals the biological importance of EFS-GINI. Through bioinformatics analysis, we shows that m5C-related genes play an important role in the occurrence and progression of renal cell carcinoma, and are expected to become an important marker to predict the prognosis of patients.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques.MethodsWe use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers.ResultsIn this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified.ConclusionsIt is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What's more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

Dataset Information

Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.

Publications

Identifying potential clinical syndromes of hepatocellular carcinoma using PSO-based hierarchical feature selection algorithm.

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