A hybrid feature selection algorithm and its application in bioinformatics

Bi-level gene selection of cancer by combining clustering and sparse learning

The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.

TRIM29 modulates proteins involved in PTEN/AKT/mTOR and JAK2/STAT3 signaling pathway and suppresses the progression of hepatocellular carcinoma

Tripartite motif-containing 29 (TRIM29), also known as the ataxia telangiectasia group D-complementing (ATDC) gene, has been reported to play an oncogenic or tumor suppressive role in developing different tumors. So far, its expression and biological functions in hepatocellular carcinoma (HCC) remain unclear. We investigated TRIM29 expression pattern in human HCC samples using quantitative RT-PCR and immunohistochemistry. Relationships between TRIM29 expression level, clinical prognostic indicators, overall survival (OS), and disease-free survival (DFS) were evaluated by Kaplan-Meier analysis and Cox proportional hazards model. A series of in vitro experiments and a xenograft tumor model were conducted to detect the functions of TRIM29 in HCC cells. RNA sequencing, western blotting, and immunochemical staining were performed to assess the molecular regulation of TRIM29 in HCC. We found that the mRNA and protein levels of TRIM29 were significantly reduced in HCC samples, compared with adjacent noncancerous tissues, and were negatively correlated with poor differentiation of HCC tissues. Survival analysis confirmed that lower TRIM29 expression significantly correlated with shorter OS and DFS of HCC patients. TRIM29 overexpression remarkably inhibited cell proliferation, migration, and EMT in HCC cells, whereas knockdown of TRIM29 reversed these effects. Moreover, deactivation of the PTEN/AKT/mTOR and JAK2/STAT3 pathways might be involved in the tumor suppressive role of TRIM29 in HCC. Our findings indicate that TRIM29 in HCC exerts its tumor suppressive effects through inhibition of the PTEN/AKT/mTOR and JAK2/STAT3 signaling pathways and may be used as a potential biomarker for survival in patients with HCC.

A Feature Selection Method Based on Graph Theory for Cancer Classification

OBJECTIVE

Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification.

METHODS

In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data.

RESULTS

In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods.

CONCLUSION

In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization

BACKGROUND

In the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification information.

RESULTS

We use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray datasets.

METHOD

This paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population diversity.

CONCLUSION

Compared with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of our proposed algorithmic model.

Identification of gene signatures for COAD using feature selection and Bayesian network approaches

The combination of TCGA and GTEx databases will provide more comprehensive information for characterizing the human genome in health and disease, especially for underlying the cancer genetic alterations. Here we analyzed the gene expression profile of COAD in both tumor samples from TCGA and normal colon tissues from GTEx. Using the SNR-PPFS feature selection algorithms, we discovered a 38 gene signatures that performed well in distinguishing COAD tumors from normal samples. Bayesian network of the 38 genes revealed that DEGs with similar expression patterns or functions interacted more closely. We identified 14 up-DEGs that were significantly correlated with tumor stages. Cox regression analysis demonstrated that tumor stage, STMN4 and FAM135B dysregulation were independent prognostic factors for COAD survival outcomes. Overall, this study indicates that using feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.