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Title: Discovery of novel molecular targets in cancer using bioinformatics.
Creator: De Young, Maurice Phillip, V., Florida Atlantic University, Narayanan, Ramaswamy
Abstract/Description: The Cancer Genome Anatomy Project (CGAP) database of the National Cancer Institute contains thousands of expressed sequences, both known and novel, derived from diverse sets of normal, precancerous, and tumor cDNA libraries. This offers the possibility of using this database as a rational starting point for bioinformatics-based cancer gene discovery. Using the Digital Differential Display tool of the CGAP database, a hypothesis-driven gene discovery approach was undertaken to analyze...
Show moreThe Cancer Genome Anatomy Project (CGAP) database of the National Cancer Institute contains thousands of expressed sequences, both known and novel, derived from diverse sets of normal, precancerous, and tumor cDNA libraries. This offers the possibility of using this database as a rational starting point for bioinformatics-based cancer gene discovery. Using the Digital Differential Display tool of the CGAP database, a hypothesis-driven gene discovery approach was undertaken to analyze differential expression of various solid tumor types. Two hundred known genes and five hundred novel sequences were discovered to be differentially expressed, and a comprehensive database was established to facilitate identification of cancer diagnostic and therapeutic targets. To validate the use of bioinformatics in discovering genes with organ- and tumor-selectivity, novel ESTs predicted to be colon tumor-specific were analyzed further for expression specificity. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) analysis using matched sets of colon normal- and tumor-derived cDNAs identified one EST to be specifically expressed in the majority of colon tumors and normal small intestine. Due to this apparent specificity, the gene was termed Colon Carcinoma Related Gene (CCRG). Based on protein sequence analysis, CCRG belongs to a novel class of secreted factors. Another gene identified in this study showed homology to Single Minded 2 gene (SIM2). Involvement between SIM2 and cancer has not yet been reported. Isoform-specific expression of SIM2 short-form (SIM2-s) was seen in colon, pancreas, and prostate carcinomas but not in most normal tissues. Using a large collection of paraffin sections from colon, pancreas, and prostate tumor and normal tissues, elevated protein expression was seen in tumors compared to normal tissue specimens, demonstrating the diagnostic potential of SIM2-s. Antisense inhibition of SIM2-s expression in colon and pancreatic cancer cell lines caused inhibition of gene expression, growth inhibition, and apoptosis. Administration of SIM2-s antisense in nude mice caused inhibition of colon tumor growth without pronounced gross toxicity. Using GeneChipRTM technology, a gene expression profile indicative of apoptosis was observed in the colon cancer model. CCRG and SIM2-s offer both a diagnostic and therapeutic potential in select cancers and validate the use of bioinformatics approaches in the gene discovery paradigm.
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Date Issued: 2003
PURL: http://purl.flvc.org/fau/fd/FADT12053
Subject Headings: Bioinformatics, Gene expression, Oncogenes
Format: Document (PDF)

Title: WHOLE MITOCHONDRIAL GENOMES OF WILD CERCOPITHECUS MONKEYS FROM THE CONGO BASIN.
Creator: Parke, Stacy-Anne, Detwiler, Kate M., Florida Atlantic University, Department of Anthropology, Dorothy F. Schmidt College of Arts and Letters
Abstract/Description: Bioinformatics tools applied to large-scale genomic datasets have helped develop our understanding of primate phylogenetics. However, it is becoming increasingly evident that biological data are accumulating faster than the current capacity of the bioanthropological community to analyze, integrate, and mine the data. Subsequently, this affects how anthropologists create and distribute knowledge. There is a growing need for more training in bioinformatics within anthropological spaces and the...
Show moreBioinformatics tools applied to large-scale genomic datasets have helped develop our understanding of primate phylogenetics. However, it is becoming increasingly evident that biological data are accumulating faster than the current capacity of the bioanthropological community to analyze, integrate, and mine the data. Subsequently, this affects how anthropologists create and distribute knowledge. There is a growing need for more training in bioinformatics within anthropological spaces and the development of user-friendly bioinformatic tools for analysis, mining, and modeling of both local and global datasets. This thesis showcases the use of (applied) bioinformatics tools to construct seven new whole mitochondrial genomes to study primate variation. Furthermore, this thesis entails an investigation of the guenon radiation to develop and document bioinformatics and statistical tools to perform a phylogenetic analysis of the genus Cercopithecus. Finally, the utility of the pipelines for other researchers in the Detwiler Lab Group and the potential for further phylogenetic studies are discussed.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00014041
Subject Headings: Cercopithecus, Bioinformatics, Monkeys, Congo
Format: Document (PDF)

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)

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)

Title: Ensemble Learning Algorithms for the Analysis of Bioinformatics Data.
Creator: Fazelpour, Alireza, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Developments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset...
Show moreDevelopments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset size, noisy data, and complexity of data in terms of hard to distinguish decision boundaries between classes within the data. In recognition of the aforementioned challenges, this dissertation utilizes a variety of machine-learning and data-mining techniques, such as ensemble classification algorithms in conjunction with data sampling and feature selection techniques to alleviate these problems, while improving the classification results of models built on these datasets. However, in building classification models researchers and practitioners encounter the challenge that there is not a single classifier that performs relatively well in all cases. Thus, numerous classification approaches, such as ensemble learning methods, have been developed to address this problem successfully in a majority of circumstances. Ensemble learning is a promising technique that generates multiple classification models and then combines their decisions into a single final result. Ensemble learning often performs better than single-base classifiers in performing classification tasks. This dissertation conducts thorough empirical research by implementing a series of case studies to evaluate how ensemble learning techniques can be utilized to enhance overall classification performance, as well as improve the generalization ability of ensemble models. This dissertation investigates ensemble learning techniques of the boosting, bagging, and random forest algorithms, and proposes a number of modifications to the existing ensemble techniques in order to improve further the classification results. This dissertation examines the effectiveness of ensemble learning techniques on accounting for challenging characteristics of class imbalance and difficult-to-learn class decision boundaries. Next, it looks into ensemble methods that are relatively tolerant to class noise, and not only can account for the problem of class noise, but improves classification performance. This dissertation also examines the joint effects of data sampling along with ensemble techniques on whether sampling techniques can further improve classification performance of built ensemble models.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004588
Subject Headings: Bioinformatics., Data mining -- Technological innovations., Machine learning.
Format: Document (PDF)

Title: Analysis of gene expression profiles from normal and chronic lymphocytic leukemia white blood cells.
Creator: Chapp, Robert James., Florida Atlantic University, Hartmann, James X.
Abstract/Description: This project used a bioinformatics approach to identify the genetic differential expression of chronic lymphocytic leukemia (CLL) white blood cells as compared to normal white blood cells. Several public access databases and data mining tools were used to collect these data. The data collected was validated by independent bioinformatics databases and the methodology was supported by previously published "gene chip" differential expression data. This research identifies a pattern of...
Show moreThis project used a bioinformatics approach to identify the genetic differential expression of chronic lymphocytic leukemia (CLL) white blood cells as compared to normal white blood cells. Several public access databases and data mining tools were used to collect these data. The data collected was validated by independent bioinformatics databases and the methodology was supported by previously published "gene chip" differential expression data. This research identifies a pattern of differential gene expression specific to CLL white blood cells that can be used for the early diagnosis of CLL. The study also identifies the probable genetic origin for the low expression of tyrosine kinase and IgM immunoglobulin observed in CLL B-cells. Also presented are genes associated with chromosomal regions previously reported as deleted in a high percentage of CLL cases. These data can be used in further research and for the treatment of CLL.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13024
Subject Headings: Lymphocytic leukemia, Gene expression, Bioinformatics, Leucocytes
Format: Document (PDF)

Title: Analysis of machine learning algorithms on bioinformatics data of varying quality.
Creator: Shanab, Ahmad Abu, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One...
Show moreOne of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.
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Date Issued: 2015
PURL: http://purl.flvc.org./fau/fd/FA00004425, http://purl.flvc.org/fau/fd/FA00004425
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Machine learning techniques for alleviating inherent difficulties in bioinformatics data.
Creator: Dittman, David, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: In response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field...
Show moreIn response to the massive amounts of data that make up a large number of bioinformatics datasets, it has become increasingly necessary for researchers to use computers to aid them in their endeavors. With difficulties such as high dimensionality, class imbalance, noisy data, and difficult to learn class boundaries, being present within the data, bioinformatics datasets are a challenge to work with. One potential source of assistance is the domain of data mining and machine learning, a field which focuses on working with these large amounts of data and develops techniques to discover new trends and patterns that are hidden within the data and to increases the capability of researchers and practitioners to work with this data. Within this domain there are techniques designed to eliminate irrelevant or redundant features, balance the membership of the classes, handle errors found in the data, and build predictive models for future data.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004362, http://purl.flvc.org/fau/fd/FA00004362
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: Binary representation of DNA sequences towards developing useful algorithms in bioinformatic data-mining.
Creator: Pandya, Shivani., Florida Atlantic University, Neelakanta, Perambur S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This thesis refers to a research addressing the use of binary representation of the DNA for the purpose of developing useful algorithms for Bioinformatics. Pertinent studies address the use of a binary form of the DNA base chemicals in information-theoretic base so as to identify symmetry between DNA and complementary DNA. This study also refers to "fuzzy" (codon-noncodon) considerations in delinating codon and noncodon regimes in a DNA sequences. The research envisaged further includes a...
Show moreThis thesis refers to a research addressing the use of binary representation of the DNA for the purpose of developing useful algorithms for Bioinformatics. Pertinent studies address the use of a binary form of the DNA base chemicals in information-theoretic base so as to identify symmetry between DNA and complementary DNA. This study also refers to "fuzzy" (codon-noncodon) considerations in delinating codon and noncodon regimes in a DNA sequences. The research envisaged further includes a comparative analysis of the test results on the aforesaid efforts using different statistical metrics such as Hamming distance Kullback-Leibler measure etc. the observed details supports the symmetry aspect between DNA and CDNA strands. It also demonstrates capability of identifying non-codon regions in DNA even under diffused (overlapped) fuzzy states.
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Date Issued: 2003
PURL: http://purl.flvc.org/fcla/dt/13089
Subject Headings: Bioinformatics, Data mining, Nucleotide sequence--Databases, Computer algorithms
Format: Document (PDF)

Title: Functional genomics of a novel colon carcinoma-related gene (CCRG).
Creator: Damania, Hema D., Florida Atlantic University, Narayanan, Ramaswamy, Department of Biological Sciences, Charles E. Schmidt College of Science
Abstract/Description: Recently Dr. Narayanan's laboratory, utilizing bioinformatics approaches, identified a novel gene which may play a role in colon cancer. This gene in view of its expression specificity was termed Colon Carcinoma Related Gene (CCRG). The CCRG belongs to a novel class of secreted molecules with a unique cysteine rich motif. The function of CCRG however, remains unknown. The basis of this project revolved around establishing the putative function (functional genomics) of CCRG. The rationale for...
Show moreRecently Dr. Narayanan's laboratory, utilizing bioinformatics approaches, identified a novel gene which may play a role in colon cancer. This gene in view of its expression specificity was termed Colon Carcinoma Related Gene (CCRG). The CCRG belongs to a novel class of secreted molecules with a unique cysteine rich motif. The function of CCRG however, remains unknown. The basis of this project revolved around establishing the putative function (functional genomics) of CCRG. The rationale for the project was to test a hypothesis that CCRG may offer a growth advantage to cancer cells. The availability of diverse tumor-derived cell lines, which were CCRG negative offered a possibility to study the consequence of enforced expression of CCRG. A breast carcinoma cell line was transfected with an exogenous CCRG expression vector and the stable clones were characterized. The stable transfectants of CCRG showed enhanced growth and a partial abrogation of serum growth factor(s) requirement. These results provide a framework for future experiments to further elucidate the function of CCRG.
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Date Issued: 2002
PURL: http://purl.flvc.org/fcla/dt/12965
Subject Headings: Colon (Anatomy)--Cancer, Genomics, Bioinformatics, Gene expression
Format: Document (PDF)

Title: Gapless alignment revisited.
Creator: Raley, Elizabeth Anne, Florida Atlantic University, Yu, Yi-Kuo
Abstract/Description: The purpose of sequence alignment is to detect mutual similarity, characterized by the so-called "alignment score", between sequences compared. To quantitatively assess the confidence level of an alignment result requires the knowledge of alignment score statistics under a certain null model and is the central issue in sequence alignment. In this thesis, the score statistics of Markov null model were revisited and the score statistics of non-Markov null model were investigated for two state...
Show moreThe purpose of sequence alignment is to detect mutual similarity, characterized by the so-called "alignment score", between sequences compared. To quantitatively assess the confidence level of an alignment result requires the knowledge of alignment score statistics under a certain null model and is the central issue in sequence alignment. In this thesis, the score statistics of Markov null model were revisited and the score statistics of non-Markov null model were investigated for two state-of-the-art algorithms, namely, the gapless Smith-Waterman and Hybrid algorithms. These two algorithms were further used to find highly related signals in unrelated sequences and in weakly related sequences corresponding, respectively, to Markov null model and non-Markov null model. The confidence levels of these models were also studied. Since the sequence similarity we are interested in comes from evolutionary history, we also investigated the relationship between sequence alignment, the tool to find similarity, and evolution. The average evolution distance between the daughter sequences was found and compared with their expected values, for individual trees and as an average over many trees.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12856
Subject Headings: Bioinformatics, Amino acid sequence--Databases, Markov processes
Format: Document (PDF)

Title: Studies on information-theoretics based data-sequence pattern-discriminant algorithms: Applications in bioinformatic data mining.
Creator: Arredondo, Tomas Vidal., Florida Atlantic University, Neelakanta, Perambur S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This research refers to studies on information-theoretic (IT) aspects of data-sequence patterns and developing thereof discriminant algorithms that enable distinguishing the features of underlying sequence patterns having characteristic, inherent stochastical attributes. The application potentials of such algorithms include bioinformatic data mining efforts. Consistent with the scope of the study as above, considered in this research are specific details on information-theoretics and entropy...
Show moreThis research refers to studies on information-theoretic (IT) aspects of data-sequence patterns and developing thereof discriminant algorithms that enable distinguishing the features of underlying sequence patterns having characteristic, inherent stochastical attributes. The application potentials of such algorithms include bioinformatic data mining efforts. Consistent with the scope of the study as above, considered in this research are specific details on information-theoretics and entropy considerations vis-a-vis sequence patterns (having stochastical attributes) such as DNA sequences of molecular biology. Applying information-theoretic concepts (essentially in Shannon's sense), the following distinct sets of metrics are developed and applied in the algorithms developed for data-sequence pattern-discrimination applications: (i) Divergence or cross-entropy algorithms of Kullback-Leibler type and of general Czizar class; (ii) statistical distance measures; (iii) ratio-metrics; (iv) Fisher type linear-discriminant measure and (v) complexity metric based on information redundancy. These measures are judiciously adopted in ascertaining codon-noncodon delineations in DNA sequences that consist of crisp and/or fuzzy nucleotide domains across their chains. The Fisher measure is also used in codon-noncodon delineation and in motif detection. Relevant algorithms are used to test DNA sequences of human and some bacterial organisms. The relative efficacy of the metrics and the algorithms is determined and discussed. The potentials of such algorithms in supplementing the prevailing methods are indicated. Scope for future studies is identified in terms of persisting open questions.
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Date Issued: 2003
PURL: http://purl.flvc.org/fau/fd/FADT12057
Subject Headings: Data mining, Bioinformatics, Discriminant analysis, Information theory in biology
Format: Document (PDF)

Title: Statistical physics inspired methods to assign statistical significance in bioinformatics and proteomics: From sequence comparison to mass spectrometry based peptide sequencing.
Creator: Alves, Gelio, Florida Atlantic University, Yu, Yi-Kuo
Abstract/Description: After the sequencing of many complete genomes, we are in a post-genomic era in which the most important task has changed from gathering genetic information to organizing the mass of data as well as under standing how components interact with each other. The former is usually undertaking using bioinformatics methods, while the latter task is generally termed proteomics. Success in both parts demands correct statistical significance assignments for results found. In my dissertation. I study two...
Show moreAfter the sequencing of many complete genomes, we are in a post-genomic era in which the most important task has changed from gathering genetic information to organizing the mass of data as well as under standing how components interact with each other. The former is usually undertaking using bioinformatics methods, while the latter task is generally termed proteomics. Success in both parts demands correct statistical significance assignments for results found. In my dissertation. I study two concrete examples: global sequence alignment statistics and peptide sequencing/identification using mass spectrometry. High-performance liquid chromatography coupled to a mass spectrometer (HPLC/MS/MS), enabling peptide identifications and thus protein identifications, has become the tool of choice in large-scale proteomics experiments. Peptide identification is usually done by database searches methods. The lack of robust statistical significance assignment among current methods motivated the development of a novel de novo algorithm, RAId, whose score statistics then provide statistical significance for high scoring peptides found in our custom, enzyme-digested peptide library. The ease of incorporating post-translation modifications is another important feature of RAId. To organize the massive protein/DNA data accumulated, biologists often cluster proteins according to their similarity via tools such as sequence alignment. Homologous proteins share similar domains. To assess the similarity of two domains usually requires alignment from head to toe, ie. a global alignment. A good alignment score statistics with an appropriate null model enable us to distinguish the biologically meaningful similarity from chance similarity. There has been much progress in local alignment statistics, which characterize score statistics when alignments tend to appear as a short segment of the whole sequence. For global alignment, which is useful in domain alignment, there is still much room for exploration/improvement. Here we present a variant of the direct polymer problem in random media (DPRM) to study the score distribution of global alignment. We have demonstrate that upon proper transformation the score statistics can be characterized by Tracy-Widom distributions, which correspond to the distributions for the largest eigenvalue of various ensembles of random matrices.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12194
Subject Headings: Molecular biology--Data processing, Bioinformatics, Proteomics, Genomics
Format: Document (PDF)

Title: Analyzing the effect of fin morphology on the propulsive performance of an oscillating caudal fin using a robotic model.
Creator: Fischer, Tyler M., Curet, Oscar M., Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: A bio-inspired robotic underwater vessel was developed to test the effect of fin morphology on the propulsive performance of caudal fin. The robotic vessel, called The Bullet Fish, features a cylindrical body with a hemisphere at the forward section and a conical body at the stern. The vessel uses an oscillating caudal fin for thrust generation. The robotic vessel was tested in a recirculating flume for seven different caudal fins that range different bio-inspired forms and aspect ratios. The...
Show moreA bio-inspired robotic underwater vessel was developed to test the effect of fin morphology on the propulsive performance of caudal fin. The robotic vessel, called The Bullet Fish, features a cylindrical body with a hemisphere at the forward section and a conical body at the stern. The vessel uses an oscillating caudal fin for thrust generation. The robotic vessel was tested in a recirculating flume for seven different caudal fins that range different bio-inspired forms and aspect ratios. The experiments were performed at four different flow velocities and two flapping frequencies: 0.5 and 1.0 Hz. We found that for 1 Hz flapping frequency that in general as the aspect-ratio decreases both thrust production tends and power decrease resulting in a better propulsive efficiency for aspect ratios between 0.9 and 1.0. A less uniform trend was found for 0.5 Hz, where our data suggest multiple efficiency peaks. Additional experiments on the robotic model could help understand the propulsion aquatic locomotion and help the design of bio-inspired underwater vehicles.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004944, http://purl.flvc.org/fau/fd/FA00004944
Subject Headings: Robotics., Robots--Kinematics., Artificial intelligence., Biomimetics., Bioinformatics., Stereotypes (Social psychology)
Format: Document (PDF)

Title: Biological Computation: the development of a genomic analysis pipeline to identify cellular genes modulated by the transcription / splicing factor srsf1.
Creator: Clark, Evan, Asghar, Waseem, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: SRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and...
Show moreSRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and analyzed both the short term (12 hours) and long term (48 hours) effects of SRSF1 expression in a human cell line. Furthermore, we analyzed and compared the effect of the expression of a naturally occurring deletion mutant of SRSF1 (RRM12) to the full-¬‐length protein. Our analysis reveals that shortly after SRSF1 is over-¬‐expressed the transcription of several histone coding genes is down-¬‐regulated, allowing for a more relaxed chromatin state and efficient transcription by RNA Polymerase II. This effect is reversed at 48 hours. At the same time key genes for the immune pathways are activated, more notably Tumor Necrosis Factor-¬‐Alpha (TNF-¬‐α), suggesting a role for SRSF1 in T cell functions.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004858, http://purl.flvc.org/fau/fd/FA00004858
Subject Headings: Gene expression., Computational biology., Markov processes., Bioinformatics., Genetic engineering., Molecular biology.
Format: Document (PDF)

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)

Title: Statistical and Entropy Considerations for Ultrasound Tissue Characterization.
Creator: Navumenka, Khrystsina, Aalo, Valentine A., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Modern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been...
Show moreModern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been found to be a good statistical tool to analyze the envelope and/or the intensity of backscattered signal in ultrasound tissue characterization. However, its use has usually been limited due to the fact that its probability density function (PDF) is not available in closed-form. In this work we present a novel closed-form representation for the H-K distribution. In addition, we propose using the first order approximation of the H-K distribution, the I-K distribution that has a closed-form, for the ultrasound tissue characterization applications. More specifically, we show that some tissue conditions that cause the backscattered signal to have low effective density values, can be successfully modeled by the I-K PDF. We introduce the concept of using H-K PDF-based and I-K PDF-based entropies as additional tools for characterization of ultrasonic breast tissue images. The entropy may be used as a goodness of fit measure that allows to select a better-fitting statistical model for a specific data set. In addition, the values of the entropies as well as the values of the statistical distribution parameters, allow for more accurate classification of tumors.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004922, http://purl.flvc.org/fau/fd/FA00004922
Subject Headings: Ultrasonics in medicine., Artificial intelligence., Computer vision in medicine., Diagnostic ultrasonic imaging., Bioinformatics.
Format: Document (PDF)

Title: Bioinformatics mining of the dark matter proteome for cancer targets discovery.
Creator: Delgado, Ana Paula, Narayanan, Ramaswamy, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Biological Sciences
Abstract/Description: Mining the human genome for therapeutic target(s) discovery promises novel outcome. Over half of the proteins in the human genome however, remain uncharacterized. These proteins offer a potential for new target(s) discovery for diverse diseases. Additional targets for cancer diagnosis and therapy are urgently needed to help move away from the cytotoxic era to a targeted therapy approach. Bioinformatics and proteomics approaches can be used to characterize novel sequences in the genome...
Show moreMining the human genome for therapeutic target(s) discovery promises novel outcome. Over half of the proteins in the human genome however, remain uncharacterized. These proteins offer a potential for new target(s) discovery for diverse diseases. Additional targets for cancer diagnosis and therapy are urgently needed to help move away from the cytotoxic era to a targeted therapy approach. Bioinformatics and proteomics approaches can be used to characterize novel sequences in the genome database to infer putative function. The hypothesis that the amino acid motifs and proteins domains of the uncharacterized proteins can be used as a starting point to predict putative function of these proteins provided the framework for the research discussed in this dissertation.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004361, http://purl.flvc.org/fau/fd/FA00004361
Subject Headings: Bioinformatics, Cancer -- Genetic aspects, Drug development -- Data processing, Genomics, Medical informatics, Proteomes -- Data processing, Tumors -- Immunological aspects
Format: Document (PDF)

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)

Title: Adaptive energy-aware real-time detection models for cardiac atrial fibrillation.
Creator: Bouhenguel, Redjem., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Though several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ;...
Show moreThough several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ; they require long battery life, and ultimately fall short on delivering real-time continuous detection of arrhythmia and specifically progressive development of cardiac A-Fib. The focus of this dissertation is the design of a class of adaptive and efficient energy-aware real-time detection models for monitoring, early real-time detection and reporting of progressive development of cardiac A-Fib.... The design promises to have a greater positive public health impact from predicting A-Fib and providing a viable approach to meeting the energy needs of current and future real-time monitoring, detecting and reporting required in wearable computing healthcare applications that are constrained by scarce energy resources.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3358332
Subject Headings: Medical informatics, Medicine, Data processing, Imaging systems in medicine, Design and construction, Cardiovascular system, Diseases, Diagnosis, Bioinformatics
Format: Document (PDF)

FAU Theses and Dissertations (20)	+ -
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Bioinformatics (16)	+ -
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DNA microarrays (4)	+ -
Genomics (4)	+ -
Data mining (3)	+ -

Gene expression (3)	+ -
Artificial intelligence (2)	+ -
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Data processing (2)	+ -
Gene expression. (2)	+ -
Machine learning (2)	+ -
Medical informatics (2)	+ -
Proteomics (2)	+ -
System design (2)	+ -
Theory of computation (2)	+ -
Amino acid sequence--Databases (1)	+ -
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Biomimetics. (1)	+ -
Cancer -- Genetic aspects (1)	+ -
Cardiovascular system (1)	+ -
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abstract (1)	+ -

Florida Atlantic University (16)	+ -
College of Engineering and Computer Science (11)	+ -
Department of Computer and Electrical Engineering and Computer Science (10)	+ -
Khoshgoftaar, Taghi M. (4)	+ -
Charles E. Schmidt College of Science (3)	+ -

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