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A review of the stability of feature selection techniques for bioinformatics data
Title
A review of the stability of feature selection techniques for bioinformatics data.
Creator
Awada, Wael, Khoshgoftaar, Taghi M., Dittman, David, Wald, Randall, Napolitano, Amri E., Graduate College
Date Issued
2013-04-12
PURL
http://purl.flvc.org/fcla/dt/3361293
Subject Headings
Bioinformatics, DNA microarrays, Data mining
Format
Document (PDF)
Bioinformatic analysis of viral genomic sequences and concepts of genome-specific national vaccine design
Title
Bioinformatic analysis of viral genomic sequences and concepts of genome-specific national vaccine design.
Creator
Chatterjee, Sharmistha P., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
This research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information...
Show moreThis research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information-theoretics (entropy principles) to analyze genomic sequences; (iii) envisaging various aspects of biothermodynamic energetics so as to determine the framework and architecture that decide the stability and patterns of the subsequences in a genome; (iv) evaluating the genomic details using spectral-domain techniques; (v) studying fuzzy considerations to ascertain the overlapping details in genomic sequences; (vi) determining the common subsequences among various strains of a virus by logistically regressing the data obtained via entropic, energetics and spectral-domain exercises; (vii) differentiating informational profiles of coding and non-coding regions in a DNA sequence to locate aberrant (cryptic) attributes evolved as a result of mutational changes and (viii) finding the signatures of CDS of genomes of viral strains toward rationally conceiving plausible designs of vaccines. Commensurate with the topics indicated above, necessary simulations are proposed and computational exercises are performed (with MatLabTM R2009b and other software as needed). Extensive data gathered from open-literature are used thereof and, simulation results are verified. Lastly, results are discussed, inferences are made and open-questions are identified for future research.
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Date Issued
2013
PURL
http://purl.flvc.org/FAU/3360772
Subject Headings
Genetic engineering, Bioinformatics, Genomics, DNA microarrays, Proteomics
Format
Document (PDF)
Relationships between the global structure of genetic networks and mRNA levels measured by cDNA microarrays
Title
Relationships between the global structure of genetic networks and mRNA levels measured by cDNA microarrays.
Creator
Liebovitch, Larry S., Shehadeh, Lina A., Jirsa, Viktor K.
Date Issued
2006-05-16
PURL
http://purl.flvc.org/fau/165446
Subject Headings
DNA microarrays, DNA--genetics, Gene Expression Regulation, DNA- protein interactions, Genetic regulation, Messenger RNA--Analysis
Format
Document (PDF)
Gene selection for sample sets with biased distribution
Title
Gene selection for sample sets with biased distribution.
Creator
Kamal, Abu Hena Mustafa., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Microarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the...
Show moreMicroarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the selection process. In biomedical science, it is very common to have microarray expression data which is severely biased with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). These sample sets with biased distributions require special attention from researchers for identification of genes responsible for a particular disease. In this thesis, we propose three filtering techniques, Higher Weight ReliefF, ReliefF with Differential Minority Repeat and ReliefF with Balanced Minority Repeat to identify genes responsible for fatal diseases from biased microarray expression data. Our solutions are evaluated on five well-known microarray datasets, Colon, Central Nervous System, DLBCL Tumor, Lymphoma and ECML Pancreas. Experimental comparisons with the traditional ReliefF filtering method demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions.
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Date Issued
2009
PURL
http://purl.flvc.org/FAU/186330
Subject Headings
Gene expression, Research, Methodology, Medical informatics, Apoptosis, Molecular aspects, DNA microarrays, Research
Format
Document (PDF)
Activators and repressors of transcription
Title
Activators and repressors of transcription: using bioinformatics approaches to analyze and group human transcription factors.
Creator
Savitskaya, Ala., Charles E. Schmidt College of Science, Department of Biological Sciences
Abstract/Description
Transcription factors are macromolecules that are involved in transcriptional regulation by interacting with specific DNA regions, and they can cause activation or silencing of their target genes. Gene regulation by transcriptional control explains different biological processes such as development, function, and disease. Even though transcriptional control has been of great interest for molecular biology, much still remains unknown. This study was designed to generate the most current list...
Show moreTranscription factors are macromolecules that are involved in transcriptional regulation by interacting with specific DNA regions, and they can cause activation or silencing of their target genes. Gene regulation by transcriptional control explains different biological processes such as development, function, and disease. Even though transcriptional control has been of great interest for molecular biology, much still remains unknown. This study was designed to generate the most current list of human transcription factor genes. Unique entries of transcription factor genes were collected and entered into Microsoft Office 2007 Access Database along with information about each gene. Microsoft Office 2007 Access tools were used to analyze and group collected entries according to different properties such as activator or repressor record, or presence of certain protein domains. Furthermore, protein sequence alignments of members of different groups were performed, and phylogenetic trees were used to analyze relationship between different members of each group. This work contributes to the existing knowledge of transcriptional regulation in humans.
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Date Issued
2010
PURL
http://purl.flvc.org/FAU/1930495
Subject Headings
Transcription factors, Genetic transcription, Regulation, Cellular signal transduction, DNA microarrays, Bioinformatics
Format
Document (PDF)
Bioinformatics-inspired binary image correlation
Title
Bioinformatics-inspired binary image correlation: application to bio-/medical-images, microsarrays, finger-prints and signature classifications.
Creator
Pappusetty, Deepti, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image...
Show moreThe efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies.
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Date Issued
2011
PURL
http://purl.flvc.org/FAU/3333052
Subject Headings
Bioinformatics, Statistical methods, Diagnostic imaging, Digital techniques, Image processing, Digital techniques, Pattern perception, Data processing, DNA microarrays
Format
Document (PDF)
The Single Minded 2 Gene (SIM2) and Cancer: Harnessing Micro-Array Data to Facilitate Pathway Discovery and Validation
Title
The Single Minded 2 Gene (SIM2) and Cancer: Harnessing Micro-Array Data to Facilitate Pathway Discovery and Validation.
Creator
Aleman, Mireille J., Narayanan, Ramaswamy, Florida Atlantic University
Abstract/Description
A Down's Syndrome related Single Minded 2 gene (SIM2), previously known to be associated with Trisomy 21 was predicted by bioinformatics to be colon cancer specific. In previous work from the laboratory using a patient tissue repository, an isoform of this gene, short form (SIM2-s) was shown to be colon cancer specific. Inhibition of SIM2-s expression by antisense technology resulted in cancer-cell specific apoptosis within 24 hours. Microarray-based gene expression profiling of the antisense...
Show moreA Down's Syndrome related Single Minded 2 gene (SIM2), previously known to be associated with Trisomy 21 was predicted by bioinformatics to be colon cancer specific. In previous work from the laboratory using a patient tissue repository, an isoform of this gene, short form (SIM2-s) was shown to be colon cancer specific. Inhibition of SIM2-s expression by antisense technology resulted in cancer-cell specific apoptosis within 24 hours. Microarray-based gene expression profiling of the antisense-treated colon cancer cells provided a fingerprint of genes involving key cell cycle, apoptosis, DNA damage and differentiation genes. Taking hints from the microarray database, experiments were initiated to decipher the molecular mechanism underlying the cancer specific function of the SIM2-s gene. Using an isogenic cell system, apoptosis was found to be dependent on DNA damage and repair gene, GADD45-a. Further, key pathways including p38 MAP kinase (MAPK) and specific caspases were essential for apoptosis. Programmed cell death was not dependant on cell cycle and was preceded by the induction of terminal differentiation. To clarify whether SIM2-s function is a critical determinant of differentiation, stable transfectants of SIM2-s were established in a murine adipocytic cell line (3T3-L 1 ). SIM2-s overexpression caused a pronounced block of differentiation of the pre-adipocytes into mature adipocytes. A study of the differentiation pathway in 3T3-L 1 cells suggested that this block occurs early on in the cascade. These results supported the starting premise that SIM2-s is a critical mediator of cell differentiation. To clarify whether the SIM2-s gene has transforming potential, the SIM2-s gene was overexpressed in the NIH3T3 murine fibroblast cell line. The cells expressing the human SIM2-s gene exhibited shorter doubling time, abrogation of growth serum requirement, greater cell number at saturation density and focus formation. In vivo tumorigenicity assays showed tumor formation with long latency. These results provide strong evidence for the role of SIM2-s gene in tumor cell growth and differentiation, and validate drug therapy use for the gene.
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Date Issued
2007
PURL
http://purl.flvc.org/fau/fd/FA00000845
Subject Headings
Cancer--Genetic aspects, DNA microarrays--Diagnostic use, Apoptosis--Molecular aspects, Medical informatics, Gene expression--Research--Methodology
Format
Document (PDF)
Molecular pathway identification using microarray technology
Title
Molecular pathway identification using microarray technology.
Creator
Tress, Matthew David., Florida Atlantic University, Narayanan, Ramaswamy
Abstract/Description
Harnessing the human genome using bioinformatics lead to the discovery of a highly cancer-selective gene, Single Minded 2 gene (SIM2). An isoform of the SIM2 gene, the short-form (SIM2-s), was shown to be specific to colon, pancreas, and prostate tumors. Antisense inhibition of SIM2-s in a colon carcinoma derived cell line (RKO) caused inhibition of gene expression, growth inhibition and apoptosis in vitro and in nude mice tumorigenicity models. To understand the mechanism of Sim2-s antisense...
Show moreHarnessing the human genome using bioinformatics lead to the discovery of a highly cancer-selective gene, Single Minded 2 gene (SIM2). An isoform of the SIM2 gene, the short-form (SIM2-s), was shown to be specific to colon, pancreas, and prostate tumors. Antisense inhibition of SIM2-s in a colon carcinoma derived cell line (RKO) caused inhibition of gene expression, growth inhibition and apoptosis in vitro and in nude mice tumorigenicity models. To understand the mechanism of Sim2-s antisense, the antisense treated RKO colon cancer cells were monitored for genome wide expression using Affymetrix GeneChipRTM technology. A list of apoptosis related genes was generated using GeneSpringRTM software. Select GeneChip RTM output was validated by Quantitative RT-PCR. Relevance of a key gene, Growth arrest and DNA damage inducible (GADD45a), in the SIM2-s pathway was established. These results will provide a basis for the future experiments to understand the mechanism underlying Sim2-s activation in specific tumors.
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Date Issued
2004
PURL
http://purl.flvc.org/fcla/dt/13146
Subject Headings
Medical informatics, DNA microarrays--Diagnostic use, Cancer--Genetic aspects, Apoptosis--Molecular aspects, Human genetics--Variation, Gene expression--Research--Methodology
Format
Document (PDF)