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Using repeated measurements to validate hierarchical gene clusters

BIOINFORMATICS 2008年第5期24卷 682-688页

作者： Brehelin, Leurent Gascuel, Olivier Martin, Olivier Univ Montpellier 2 CNRS LIRMM F-34060 Montpellier 1 France INRA Unite Proteom F-34060 Montpellier France INRA Unite Biostat & Proc Spatiaux F-84914 Avignon 9 France

Motivation: Hierarchical clustering is a common approach to study protein and gene expression data. This unsupervised technique is used to find clusters of genes or proteins which are expressed in a coordinated manner across a set of conditions. Because of both the biological and technical variability, experimental repetitions are generally performed. In this work, we propose an approach to evaluate the stability of clusters derived from hierarchical clustering by taking repeated measurements into account. Results: The method is based on the bootstrap technique that is used to obtain pseudo-hierarchies of genes from resampled datasets. Based on a fast dynamic programming algorithm, we compare the original hierarchy to the pseudo-hierarchies and assess the stability of the original gene clusters. Then a shuffling procedure can be used to assess the significance of the cluster stabilities. Our approach is illustrated on simulated data and on two microarray datasets. Compared to the standard hierarchical clustering methodology, it allows to point out the dubious and stable clusters, and thus avoids misleading interpretations.

关键词： microarray data EXPRESSION PATTERNS RELIABILITY MIXTURE NETWORK

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HYBRIDISING GENETIC ALGORITHM-NEURAL NETWORK (GANN) IN MARKER GENES DETECTION

HYBRIDISING GENETIC ALGORITHM-NEURAL NETWORK (GANN) IN MARKE...

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International Conference on Machine Learning and Cybernetics

作者： Tong, Dong-Ling Bournemouth Univ Sch Design Engn & Comp Poole BH12 5BB Dorset England

ISBN: (纸本)9781424447053

The identification of marker genes trigger the growth of mutated cells has received a significant attention from both medical and computing communities. Through the identified genes, the pathology of mutated cells can be revealed and precautions can be taken to prevent further proliferation of abnormal cells. In this paper, we propose an innovative gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia cancer. Our approach able to provide a sharper focus on a group of highly expressed genes in leukaemia dataset and the identified genes have been proven significant to the study of leukaemia cancer development.

关键词： Genetic algorithms neural networks microarray data gene selection fitness optimisation

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Systems biology via redescription and ontologies (I): Finding phase changes with applications to malaria temporal data

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Systems and Synthetic Biology 2007年第4期1卷 197-205页

作者： Kleinberg, Samantha Casey, Kevin Mishra, Bud Courant Institute of Mathematical Sciences New York University New York NY 10012 715 Broadway United States New York University School of Medicine New York NY United States

Biological systems are complex and often composed of many subtly interacting components. Furthermore, such systems evolve through time and, as the underlying biology executes its genetic program, the relationships between components change and undergo dynamic reorganization. Characterizing these relationships precisely is a challenging task, but one that must be undertaken if we are to understand these systems in sufficient detail. One set of tools that may prove useful are the formal principles of model building and checking, which could allow the biologist to frame these inherently temporal questions in a sufficiently rigorous framework. In response to these challenges, GOALIE (Gene ontology algorithmic logic and information extractor) was developed and has been successfully employed in the analysis of high throughput biological data (e.g. time-course gene-expression microarray data and neural spike train recordings). The method has applications to a wide variety of temporal data, indeed any data for which there exist ontological descriptions. This paper describes the algorithms behind GOALIE and its use in the study of the Intraerythrocytic Developmental Cycle (IDC) of Plasmodium falciparum, the parasite responsible for a deadly form of chloroquine resistant malaria. We focus in particular on the problem of finding phase changes, times of reorganization of transcriptional control. © 2008 The Author(s).

关键词： Information theory microarray data Model checking Ontology Redescription Timecourse data

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Neural Network Based Gene Regulatory Network Reconstruction 3

Neural Network Based Gene Regulatory Network Reconstruction

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3rd International Conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Mandal, Sudip Saha, Goutam Pal, Rajat Kumar GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479944453

Gene Regulatory Networks (GRN) is used to model the regulations in living organisms. Inferring genetic network from different experimental high throughput biological data (like microarray) is a challenging job for all researchers. In this paper, Artificial Neural Network, which is a very effective soft computing tool to learn and model the dynamics or dependencies between genes, is used for reconstruction of small scale GRN from the reduced microarray dataset of Lung Adenocarcinoma. The significances of regulations of one gene to other genes of the system are expressed by a weight matrix which is computed using Perceptron based biologically significant weight updating method by minimizing the error during learning. Based on the values of elements of filtered weight matrix, a directed weighted graph can be drawn successfully that denotes gene regulatory network.

关键词： Gene Regulatory Network microarray data Neural Network

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A DSRPCL-SVM Approach to Informative Gene Analysis

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Genomics, Proteomics & Bioinformatics 2008年第2期6卷 83-90页

作者： Wei Xiong Zhibin Cai Jinwen Ma School of Mathematical Sciences and Laboratory of Mathematics and Applied Mathematics （LMAM） PekingUniversity Beijing 100871 China

microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning （DSRPCL） algorithm and then detect the informative gene cluster or set with the help of support vector machine （SVM）. Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis.

关键词： microarray data informative gene selection clustering analysis DSRPCL tumordiagnosis

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Protein-protein interaction network analysis and identifying regulation microRNAs in asthmatic children

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ALLERGOLOGIA ET IMMUNOPATHOLOGIA 2015年第6期43卷 584-592页

作者： Chen, Y. Qiao, J. Shanghai Jiao Tong Univ Sch Med Shanghai Peoples Hosp 1 Dept Resp Med Shanghai 200080 Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Peoples Hosp 9 Dept Resp Med Shanghai 200011 Peoples R China

Background: Asthma is a chronic inflammatory airway disease, the incidence of which has increased recently. In order to identify the potential biomarkers for allergic asthma therapy, microarray data were analysed to find meaningful information. Methods: microarray data GSE18965 were downloaded from Gene Expression Ominibus (GEO), including seven asthmatic epithelium samples from children with allergic asthma and nine healthy controls. Limma package was used to detect differentially expressed genes (DEGs) and the criteria were vertical bar log fold change vertical bar >0.5 and p value < 0.05. We used database for Annotation, Visualization and Integrated Discovery (DAVID) tool to perform GO function and KEGG pathway analysis. STRING database was used to construct protein-protein interaction (PPI) network. MicroRNA (miRNA) regulation network was constructed according to miRecords database. Results: We identified 274 DEGs in asthma epithelium samples comparing with healthy controls. There were 123 up-regulated DEGs and 151 down-regulated DEGs. PPI network analysis showed that TSPO, G6PD and TXN had higher degree. miRNA regulation network demonstrated that miR-16 and miR-15a had higher degree. The target genes of miRNAs were significantly enriched in the apoptosis function. Conclusions: TSPO, G6PD and TXN, miR-16, miR-15a and apoptosis may be used as the targets for children's allergic asthma therapy. (C) 2014 SEICAP. Published by Elsevier Espana, S.L.U. All rights reserved.

关键词： Asthmatic epithelium microarray data Differentially expressed genes Protein-protein interaction network MicroRNA

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Computing the maximum similarity bi-clusters of gene expression data

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BIOINFORMATICS 2007年第1期23卷 50-56页

作者： Liu, Xiaowen Wang, Lusheng City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

Motivations: Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions. Results: We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters.

关键词： microarray data

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Genetic Algorithms and Feature Selection for Improving the Classification Performance in Healthcare

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第3期15卷 737-744页

作者： Alassaf, Alaa Alarbeed, Eman Alrasheed, Ghady Almirdasie, Abdulsalam Almutairi, Shahd Al-Hagery, Mohammed Abullah Saeed, Faisal Qassim Univ Coll Comp Dept Comp Sci Qasim Saudi Arabia Birmingham City Univ Coll Comp & Digital Technol Birmingham W Midlands England Qassim Univ Deanship Sci Res Qasim Saudi Arabia

microarray technology appeared recently and is used in genetic research to study gene expressions. microarray has been widely applied to many fields, especially the health sector, such as diagnosing and predicting diseases, specifically cancer diseases. These experiments usually generate a huge amount of gene expression data with analytical and computational complexities. Therefore, feature selection techniques and different classifications help solve these problems by eliminating irrelevant and redundant features. This paper presents a proposed method for classifying the data using eight classifications machine learning algorithms. Then, the Genetic Algorithm (GA) is applied to improve the selection of the best features and parameters for the model. We use the higher accuracy of the model among the different classifications as a measure of fit in the genetic algorithm;this means that the model's accuracy can be used to select the best solutions than others in the community. The proposed method was applied to the colon, breast, prostate, and Central Nervous System (CNS) diseases and experimental outcomes demonstrated an accuracy rate of 93.75, 96.15, 82.76, and 93.33 respectively. Based on these findings, the proposed method works well and effectively.

关键词： Cancer classification gene expression feature selection microarray data algorithm machine learning genetic algorithm

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Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients

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GENE REPORTS 2021年 22卷 100980页

作者： Ramesh, Priyanka Veerappapillai, Shanthi Karuppasamy, Ramanathan Vellore Inst Technol Sch Bio Sci & Technol Dept Biotechnol Vellore Tamil Nadu India

The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as P-value and log2 fold change values of less than 0.05 and 1.5 respectively. Further, network analysis and GO studies showed that differential expression of two hub genes namely ELANE and LTF which could induce higher levels of pro-inflammatory cytokines in the lungs. We are certain that differential expression of ELANE and LTF results in an excessive inflammatory reaction known as the cytokine storm and ultimately leading to death. Therefore, targeting these key drivers of cytokine storm genes appears to be the potential therapeutic targets for combating the Severe Acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection ultimately resulting in reduced mortality. Indeed, this predictive view may open new insights for designing an immune intervention for COVID-19 in the near future resulting in the mitigation of mortality rate.

关键词： SARS-CoV-2 microarray data Hub gene identification Cytokine storm STRING database

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A note on oligonucleotide expression values not being normally distributed

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BIOSTATISTICS 2009年第3期10卷 446-450页

作者： Hardin, Johanna Wilson, Jason Pomona Coll Dept Math Claremont CA 91711 USA Biola Univ Dept Math La Mirada CA 90639 USA

Novel techniques for analyzing microarray data are constantly being developed. Though many of the methods contribute to biological discoveries, inability to properly evaluate the novel techniques limits their ability to advance science. Because the underlying distribution of microarray data is unknown, novel methods are typically tested against the assumed normal distribution. However, microarray data are not, in fact, normally distributed, and assuming so can have misleading consequences. Using an Affymetrix technical replicate spike-in data set, we show that oligonucleotide expression values are not normally distributed for any of the standard methods for calculating expression values. The resulting data tend to have a large proportion of skew and heavy tailed genes. Additionally, we show that standard methods can give unexpected and misleading results when the data are not well approximated by the normal distribution. Robust methods are therefore recommended when analyzing microarray data. Additionally, new techniques should be evaluated with skewed and/or heavy-tailed data distributions.

关键词： Affymetrix Distributions microarray data Nonnormality

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