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Identification of Pathways and Genes Associated with Cold and Hot Properties of Chinese Materia Medica Based on Bioinformatics Analysis

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PHYTOCHEMISTRY LETTERS 2020年 38卷 70-77页

作者： Wang, Yue Zhou, Lu Zhang, Shujing Liu, Zhenzhu Li, Caiyan Gao, Yushan Sun, Yan Zheng, Fengjie Li, Yuhang Beijing Univ Chinese Med Sch Tradit Chinese Med Beijing 100029 Peoples R China

The Chinese Materia Medica can be divided into four classes: cold, cool, warm, and hot, which mainly exhibits two opposite properties: cold and hot. This study aimed to explore the scientific essence involved in the cold and hot features of Chinese Materia Medica. The microarray data of twelve cold/hot herbs with typical cold and hot properties (six for each group) were analyzed on the Gene-Cloud of Biotechnology Information. First, we compared the differences in total gene expression profiles and identified differentially expressed genes (DEGs) between two classes of herbs. Subsequently, gene ontology (GO) enrichment analysis, pathway analysis, and gene signal network analysis were performed to screen out related pathways and core genes. This study reveals that the upregulated genes in cold herbs are downregulated in hot herbs, and the genes downregulated in cold herbs were upregulated in hot herbs. The gene expression of the two types of herbs has apparent differences, and the DEGs enrich in six systems, including metabolism, immune system, nervous system, endocrine system, cell proliferation and apoptosis, and hemorheology. PRKACA, PRKACB, PRKCA, ADCY7, PIK3R1, and MAPK1 are the core genes. These results elucidated the objectivity of the differences between cold and hot herbs at a genetic level, and identified related pathways and core genes, which may be a feasible approach for solving the qua- litative and even quantitative problems of the cold and hot properties of herbs.

关键词： Chinese Materia Medica microarray data Cold and hot properties Bioinformatics Pathway Analysis

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A Hybrid Colony Fuzzy System for Analyzing Diabetes microarray data

A Hybrid Colony Fuzzy System for Analyzing Diabetes Microarr...

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10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

作者： Kumar, P. Ganesh Vijay, S. Arul Antran Devaraj, D. Anna Univ Dept Informat Technol Reg Ctr Coimbatore Tamil Nadu India Karpagam Col Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India Kalasalingam Univ Dept Comp Sci & Engn Krishnankoil India

ISBN: (纸本)9781467358750

Treatment to diabetes using microarray data has gained much attention among the physician as it provides important information about pathological states as well as information that can lead to earlier diagnosis. But its high dimensional low sample nature poses a lot of difficulties when it is analyzed by hand and needs an automatic system. As against statistical and machine learning approaches, fuzzy expert system provides an understandable diagnostic system. An important issue in the design of fuzzy expert system is knowledge acquisition. This paper presents a hybrid colony algorithm to extract if-then rules and to form membership functions from diabetes microarray data. During the run, Ant Colony Optimization (ACO) is used to generate optimal rule set and Artificial Bee Colony (ABC) is used to evolve the points of membership function. Mutual Information is used for identification of informative genes. The performance of the proposed approach is evaluated using two diabetes microarray data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with interpretable rules when compared with other approaches.

关键词： microarray data Fuzzy Expert System Ant Colony Optimization Artificial Bee Colony Mutual Information

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An Improvement of Missing Value Imputation in DNA microarray data Using Cluster-based LLS Method

An Improvement of Missing Value Imputation in DNA Microarray...

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13th International Symposium on Communications and Information Technologies (ISCIT) - Communication and Information Technology for New Life Style Beyond the Cloud

作者： Keerin, Phimmarin Kurutach, Werasak Boongoen, Tossapon Mahanakorn Univ Technol Fac Informat Sci & Technol Bangkok Thailand Royal Thai Air Force Acad Dept Math & Comp Sci Bangkok Thailand

ISBN: (纸本)9781467355803;9781467355780

Gene expressions measured during a microarray experiment usually encounter the native problem of missing values. These are due to possible errors occurring in the primary experiments, image acquisition and interpretation processes. Leaving this unsolved may critically degrade the reliability of any consequent downstream analysis or medical application. Yet, a further study of microarray data may not be possible with many standard analysis methods that require a complete data set. This paper introduces a new method to impute missing values in microarray data. The proposed algorithm, CLLS impute, is an extension of local least squares imputation with local data clustering being incorporated for improved quality and efficiency. Gene expression data is typically represented as a matrix whose rows and columns corresponds to genes and experiments, respectively. CLLS kicks off by finding a complete dataset via the removal of rows with missing value(s). Then, gene clusters and their corresponding centroids are obtained by applying a clustering technique on the complete dataset. A set of similar genes of the target gene (with missing values) are those belonging to the cluster, whose centroid is the closest to the target. Having known this, the target gene is imputed by applying regression analysis with similar genes previously determined. Empirical evaluation with several published gene expression datasets suggest that the proposed technique performs better than the classical local least square method and recently developed techniques found in the literature.

关键词： missing value imputation microarray data clustering regression

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Developing an effective biclustering technique using an enhanced proximity measure

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2020年第1期9卷 6-6页

作者： Patowary, Pallabi Sarmah, Rosy Bhattacharyya, Dhruba K. Tezpur Univ Dept Comp Sci & Engn Tezpur Assam India

This paper introduces an enhanced version of Pearson's correlation coefficient (PCC) to achieve better biclustering-enabled co-expression analysis. The modified measure called local pearson correlation measure (LPCM) helps detect shifting, scaling, and shifting-and-scaling correlation patterns effectively over gene expression data in the presence of outlier. An LPCM-based biclustering technique called local correlation-based biclustering technique (LCBT) has also been proposed to identify biclusters of high biological significance. The biclustering results have been established both statistically and biologically using benchmarked gene expression data.

关键词： microarray data Clustering Biclustering P value GO annotation Proximity measure

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Effects of Collagen-Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

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POLYMERS 2021年第23期13卷 4117-4117页

作者： Taguchi, Y-h. Turki, Turki Chuo Univ Dept Phys Tokyo 1128551 Japan King Abdulaziz Univ Dept Comp Sci Jeddah 21589 Saudi Arabia

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen-glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

关键词： feature extraction tissue engineering microarray data applications in biology and medicine

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Profile likelihood biclustering

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ELECTRONIC JOURNAL OF STATISTICS 2020年第1期14卷 731-768页

作者： Flynn, Cheryl Perry, Patrick AT&T Labs Res New York NY 10007 USA Oscar Hlth New York NY USA

Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.

关键词： Biclustering block model profile likelihood congressional voting microarray data

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Review on Feature Selection Methods for Gene Expression data Classification 4th

Review on Feature Selection Methods for Gene Expression Data...

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4th International Conference of Reliable Information and Communication Technology (IRICT)

作者： Almutiri, Talal Saeed, Faisal Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia

ISBN: (纸本)9783030335823;9783030335816

microarray technology makes it easier for scientists to rapidly measure thousands of gene's expression levels. By analyzing these data, we can find the altered genes, thereby facilitating easy diagnosis and classification of the genetic-related diseases. However, predicting and identifying cancer types is a great challenge in the medical field. Gene expression microarray contains information that can help in this regard, but microarray data have high dimensionality problem which means a large number of genes or features and a small number of samples, also there are redundant and irrelevant features that increase the challenge of microarray analysis. This study reviewed recent studies about methods, algorithms, and limitations of feature selection for microarray gene expression classification. This study compared and focused on four aspects for each related study: datasets, feature selection methods, classifiers, and accuracy results. Feature selection methods are considered as a pre-processing step which plays a vital role in the effectiveness of a classification. This paper showed that applying filter methods such as t-Test, Pearson's Correlation Coefficient (PCC), and Bhattacharyya distance eliminate irrelevant features that help to increase classification performance and accuracy. Therefore, applying wrapper or embedded methods such as Genetic Algorithm (GA) without applying filter methods in advance could affect the effectiveness of a classification negatively.

关键词： Cancer classification Gene expression Feature selection microarray data

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Cross, Valerie Zmuda, Michael Paul, Rahul Hall, Lawrence Miami Univ Comp Sci & Software Engn Oxford OH 45056 USA Univ S Florida Comp Sci & Engn Tampa FL 33620 USA

ISBN: (纸本)9781728169323

A problem for machine learning research occurs when many possible features exist but the training data examples are very few. For example, microarray data typically have a much larger number of features, the genes, as compared to the number of training data examples, the patients. One approach is to first determine the best features for prediction and then to group features based on a measure of their relatedness. The concordance correlation coefficient has been used to place somewhat correlated features into disjoint groups of similar features. Multiple base classifiers are created by randomly picking one feature from each of the feature groups and then the collection of base classifiers is used in an ensemble classifier. Each classifier in the ensemble provides a vote. The majority vote is used to produce the final class prediction. This paper investigates grouping features using fuzzy set similarity measures as well as the concordance correlation coefficient as a relatedness measure. The performance of these different measures is compared in terms of accuracy, sensitivity, specificity, and F-measure using the ensemble classifiers created with the different relatedness measures. Four microarray gene expression data sets are used in the experiments to determine the usefulness of fuzzy set similarity measures and how they compare with the concordance correlation coefficient. Using the concordance correlation coefficient to guide clustering is not superior to fuzzy set similarity measures. Depending on the particular data set and performance measure being used, different fuzzy , set similarity measures perform better than or just as well as the concordance correlation coefficient.

关键词： feature selection fuzzy set similarity measures concordance correlation coefficient microarray data

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microarray cancer feature selection: Review, challenges and research directions

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International Journal of Cognitive Computing in Engineering 2020年 1卷 78-97页

作者： Hambali, Moshood A. Oladele, Tinuke O. Adewole, Kayode S. Department of Computer Science Federal University Wukari Taraba Nigeria Department of Computer Science University of Ilorin Kwara Nigeria

microarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed. © 2020 The Authors

关键词： Embedded Feature selection Filter microarray data microarray technology Wrapper

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Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression

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HEALTH INFORMATICS JOURNAL 2021年第1期27卷 1460458221989402-1460458221989402页

作者： Gumaei, Abdu Sammouda, Rachid Al-Rakhami, Mabrook AlSalman, Hussain El-Zaart, Ali King Saud Univ Res Chair Pervas & Mobile Comp Riyadh Saudi Arabia Taiz Univ Taif Yemen King Saud Univ Riyadh Saudi Arabia Beirut Arab Univ Beirut Lebanon

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.

关键词： prostate cancer microarray data machine learning random committee ensemble learning feature selection 10-fold cross-validation

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