Browsing by Author "Alshalalfa, Mohammed"

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    Biological Network-based Approaches to The Functional Analysis of miRNAs in Prostate Cancer
    (2013-09-25) Alshalalfa, Mohammed; Alhajj, Reda
    The cell is a highly organized system of interacting molecules that operate together in a complex and efficient manner to achieve biological functions and cellular phenotypes. Traditional biology studied these components one at a time, yielding limited insights about the way a cell functions. It is now apparent that the most effective way to understand how the cell works is to unravel how these different components work together. Recent advances in biological research have led to an explosive growth in scientific data to study and characterize the function of the different components of the cell. Thanks to high-throughput techniques, an explosive growth in the size and type of biological information are generated to reveal the internal complexity of cells. This has lead to a rapid increase in the number of computational techniques developed to mine the data and reveal functional understandings of the cell. Deciphering the molecular interactions among the cellular molecules embodies a more comprehensive view of the cellular function, and integrating heterogenous interaction networks and expression data reveals a system-level understanding of the cell behavior. This thesis focuses on integrating multiple heterogenous biological networks, in particular protein networks and miRNA-target interactions, to facilitate miRNA research. This integration layer between miRNAs and protein networks helps to study the propagation of miRNAs’ influence through the biological networks of the targets. This thesis provides a profound review of the cross-talk between miRNAs and biological networks, particularly protein networks. The role of miRNAs as part of the cellular system and their influence on functional protein modules is characterized in prostate cancer progression. In this thesis, different approaches are proposed to analyze the integration layer and provide potential applications to the genomic studies of miRNAs. The first approach predicts miRNAs with high influence on protein networks and assesses their prognostic significance. The second approach predicts protein complexes that are influences by miRNAs during prostate progression. The third approach characterizes the modulation effect of genes that encode protein partners of the protein encoded by miRNA targets. The fourth approach uses protein networks to identify miRNAs enriched in gene lists. The proposed methods reveal that integrating miRNA-target and protein networks provides a new layer of biological information that assists to reveal miRNA-target modules with potential function, and uncover principles governing miRNA-mediated regulation of targets in biological networks. The results suggest that the proposed methods are promising to reveal miRNA-mediated regulation, in the context of protein networks, involved in prostate cancer progression. This thesis shows that integrating protein networks and miRNA-target networks is a valuable source of knowledge that help researchers understand how miRNA exert their function on the cellular system. This facilitates miRNA genomic research to identify miRNAs with strong influence on the proteins regulating the cell function, and thus gain better characterization of their role in disease progression and possible utility for therapeutic purposes.
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    Cancer biomarker extraction from gene expression microarray data
    (2008) Alshalalfa, Mohammed; Alhajj, Reda
    Bioinformatics is a new field of science mainly integrating computer science, mathematics, statistics and biology where the aim is to discover knowledge hidden within biological data. One of the widely investigated biological data is gene expression microarray data. Profiling the global gene expression patterns in different tissues/ sample can be investigated in few days due to microarray technology, which can accommodate the whole genome, unlike traditional methods which may take months. However, analyzing micro array data is challenging as the number of features (genes) is very large relative to the number of attributes (samples). Fortunately, microarray has been successfully used to study gene expression data; this allowed researchers to investigate different diseases, including cancer. In other words, using microarray in cancer diagnosis showed to be very efficient and reliable, but the large number of genes makes the data noisy and difficult to deal with. Consequently, identifying relevant genes has received considerable attention. In this thesis, we combine biological knowledge with machine learning techniques to propose three methods for extracting the most informative genes for cancer classification. The first method is based on double clustering; we filter the data initially with a statistical test and then cluster the data iteratively to get the best number of clusters. The genes closest to the centroids of the resulting clusters showed to have high potential to be significant features for sample classification. These genes (one per centroid) are used as input for building a classification model. The second method is based on iterative t-test in a way that eliminates noise from the data. The third method is a hybrid approach which combines statistical tests with entropy based tests. This method uses the t-test and Singular Value Decomposition (SVD) based entropy. It showed to be effective as it considers the feature itself and its effect on the data entropy. This approach is the first to combine entropy and statistical significance for gene ranking. We have also developed SVD based gene extraction method for multi-class data; only introduced at high level in this thesis, details are left are future work. The test results reported demonstrate the applicability and effectiveness of the three proposed approaches. _x000D_ Index Terms: Classification, clustering, t-test, singular value decomposition, support vector machine, microarray data, gene expression data, over-expression, underexpress10n._x000D_
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    Coordinate MicroRNA-Mediated Regulation of Protein Complexes in Prostate Cancer
    (Public Library of Science, 2013-12-31) Alshalalfa, Mohammed; Bader, Gary D.; Bismar, Tarek A.; Alhajj, Reda
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    Cysteine- rich secretory protein 3 (CRISP3), ERG and PTEN define a molecular subtype of prostate cancer with implication to patients’ prognosis
    (BioMed Central, 2014-03-07) Bashir, Samir Al; Alshalalfa, Mohammed; Hegazy, Samar A; Dolph, Michael; Donnelly, Bryan; Bismar, Tarek A
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    Detecting Cancer Outlier Genes with Potential Rearrangement Using Gene Expression Data and Biological Networks
    (2012-06-28) Alshalalfa, Mohammed; Bismar, Tarek A.; Alhajj, Reda
    Gene alterations are a major component of the landscape of tumor genomes. To assess the significance of these alterations in the development of prostate cancer, it is necessary to identify these alterations and analyze them from systems biology perspective. Here, we present a new method (EigFusion) for predicting outlier genes with potential gene rearrangement. EigFusion demonstrated excellent performance in identifying outlier genes with potential rearrangement by testing it to synthetic and real data to evaluate performance. EigFusion was able to identify previously unrecognized genes such as FABP5 and KCNH8 and confirmed their association with primary and metastatic prostate samples while confirmed the metastatic specificity for other genes such as PAH, TOP2A, and SPINK1. We performed protein network based approaches to analyze the network context of potential rearranged genes. Functional gene rearrangement Modules are constructed by integrating functional protein networks. Rearranged genes showed to be highly connected to well-known altered genes in cancer such as AR, RB1, MYC, and BRCA1. Finally, using clinical outcome data of prostate cancer patients, potential rearranged genes demonstrated significant association with prostate cancer specific death.
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    Detecting Cancer Outlier Genes with Potential Rearrangement Using Gene Expression Data and Biological Networks
    (Hindawi Publishing Corporation, 2012-05-08) Alshalalfa, Mohammed; Bismar, Tarek A.; Alhajj, Reda
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    Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study
    (BioMed Central, 2012-08-28) Alshalalfa, Mohammed; Bader, Gary D; Goldenberg, Anna; Morris, Quaid; Alhajj, Reda
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    MicroRNA Response Elements-Mediated miRNA-miRNA Interactions in Prostate Cancer
    (2012-11-04) Alshalalfa, Mohammed
    The cell is a highly organized system of interacting molecules including proteins, mRNAs, and miRNAs. Analyzing the cellfrom a systems perspective by integrating different types of data helps revealing the complexity of diseases. Although there isemerging evidence that microRNAs have a functional role in cancer, the role of microRNAs in mediating cancer progressionand metastasis remains not fully explored. As the amount of available miRNA and mRNA gene expression data grows, moresystematic methods combining gene expression and biological networks become necessary to explore miRNA function. In this workI integrated functional miRNA-target interactions with mRNA and miRNA expression to infer mRNA-mediated miRNA-miRNAinteractions. The inferred network represents miRNA modulation through common targets. The network is used to characterizethe functional role of microRNA response element (MRE) to mediate interactions between miRNAs targeting the MRE. Resultsrevealed that miRNA-1 is a key player in regulating prostate cancer progression. 11 miRNAs were identified as diagnostic andprognostic biomarkers that act as tumor suppressor miRNAs. This work demonstrates the utility of a network analysis as opposedto differential expression to find important miRNAs that regulate prostate cancer.
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    MicroRNA Response Elements-MediatedmiRNA-miRNA Interactions in Prostate Cancer
    (Hindawi Publishing Corporation, 2012-09-29) Alshalalfa, Mohammed
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    Protein network-based Lasso regression model for the construction of disease-miRNA functional interactions
    (Springer, 2013-01-22) Qabaja, Ala; Alshalalfa, Mohammed; Bismar, Tarek A; Alhajj, Reda
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