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Identification Of Prognostic Cancer Biomarkers Through The Application Of Rna-Seq Technologies And Bioinformatics, Nathan Wong
Identification Of Prognostic Cancer Biomarkers Through The Application Of Rna-Seq Technologies And Bioinformatics, Nathan Wong
McKelvey School of Engineering Theses & Dissertations
MicroRNAs (miRNAs) are short single-stranded RNAs that function as the guide sequence of the post-transcriptional regulatory process known as the RNA-induced silencing complex (RISC), which targets mRNA sequences for degradation through complementary binding to the guide miRNA. Changes in miRNA expression have been reported as correlated with numerous biological processes, including embryonic development, cellular differentiation, and disease manifestation. In the latter case, dysregulation has been observed in response to infection by human papillomavirus (HPV), which has also been established as both oncogenic in cervical cancers and oropharyngeal cancers and favorable for overall patient survival after tumor formation. The identification of …
Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera
Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera
Wayne State University Theses
The identification of pathways that are involved in a particular phenotype helps us understand the underlying biological processes. Traditional pathway analysis techniques aim to infer the impact on individual pathways using only mRNA levels. However, recent studies showed that gene expression alone is unable to capture the whole picture of biological phenomena. At the same time, MicroRNAs (miRNAs) are newly discovered gene regulators that have shown to play an important role in diagnosis, and prognosis for different types of diseases. Current pathway analysis techniques do not take miRNAs into consideration. In this project, we investigate the effect of integrating miRNA …
Towards Elucidating The Structural Principles Of Host-Pathogen Protein-Protein Interaction Networks: A Bioinformatics Survey, Huaming Chen, Jiangning Song, Geng Sun, Jun Shen, Lei Wang
Towards Elucidating The Structural Principles Of Host-Pathogen Protein-Protein Interaction Networks: A Bioinformatics Survey, Huaming Chen, Jiangning Song, Geng Sun, Jun Shen, Lei Wang
Faculty of Engineering and Information Sciences - Papers: Part B
The ultimate goal of systems biology research area is to accurately predict the behavior of biological systems through the construction of computational models, using the related molecular-level data as the input, especially when the structural information of such biological system is available. Combining the three-dimensional (3D) structural information of the cohort of macromolecules underpinning the biological system, the researchers are poised with an unprecedented opportunity to gain a full understanding on how the molecules interact with each other, particularly for an interaction network, e.g. protein-protein interaction networks. Specifically, there are currently a limited number of studies focused on the reconstruction …
Protein Fold Classification With Grow-And-Learn Network, Özlem Polat, Zümray Dokur
Protein Fold Classification With Grow-And-Learn Network, Özlem Polat, Zümray Dokur
Turkish Journal of Electrical Engineering and Computer Sciences
Protein fold classification is an important subject in computational biology and a compelling work from the point of machine learning. To deal with such a challenging problem, in this study, we propose a solution method for the classification of protein folds using Grow-and-Learn (GAL) neural network together with one-versus-others (OvO) method. To classify the most common 27 protein folds, 125 dimensional data, constituted by the physicochemical properties of amino acids, are used. The study is conducted on a database including 694 proteins: 311 of these proteins are used for training and 383 of them for testing. Overall, the classification system …
Using Latent Semantic Analysis For Automated Keyword Extraction From Large Document Corpora, Tuğba Önal Süzek
Using Latent Semantic Analysis For Automated Keyword Extraction From Large Document Corpora, Tuğba Önal Süzek
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we describe a keyword extraction technique that uses latent semantic analysis (LSA) to identify semantically important single topic words or keywords. We compare our method against two other automated keyword extractors, Tf-idf (term frequency-inverse document frequency) and Metamap, using human-annotated keywords as a reference. Our results suggest that the LSA-based keyword extraction method performs comparably to the other techniques. Therefore, in an incremental update setting, the LSA-based keyword extraction method can be preferably used to extract keywords from text descriptions from big data when compared to existing keyword extraction methods.