Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Acid stress response (1)
- Bioinformatics (1)
- Bombay Phenotype (1)
- Cancer (1)
- Cis-regulatory motif finding (1)
-
- Clonal evolution (1)
- Convolutional neural network (1)
- Copy number aberrations (1)
- Differentially expressed gene (1)
- ETD (1)
- Equilibrium points (1)
- Gene co-expression (1)
- Gene prediction (1)
- Gene regulatory network (1)
- GeneCNN (1)
- Genetic mutations (1)
- Hardy-Weinberg equations (1)
- Lactococcus lactis MG1363 (1)
- Machine learning (1)
- Mobile genetic elements (1)
- Mobile promoters (1)
- Multinomial distribution (1)
- Ploidy (1)
- Poisonous prey (1)
- Population size (1)
- Predator-prey model (1)
- Promoter regions (1)
- Proportion of gametes (1)
- Quantum estimation (1)
- Quantum model (1)
- Publication
- Publication Type
Articles 1 - 7 of 7
Full-Text Articles in Genomics
Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone
Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone
Complex Biosystems PhD Program: Dissertations
The task of gene prediction has been largely stagnant in algorithmic improvements compared to when algorithms were first developed for predicting genes thirty years ago. Rather than iteratively improving the underlying algorithms in gene prediction tools by utilizing better performing models, most current approaches update existing tools through incorporating increasing amounts of extrinsic data to improve gene prediction performance. The traditional method of predicting genes is done using Hidden Markov Models (HMMs). These HMMs are constrained by having strict assumptions made about the independence of genes that do not always hold true. To address this, a Convolutional Neural Network (CNN) …
Statistical Methods For Resolving Intratumor Heterogeneity With Single-Cell Dna Sequencing, Alexander Davis
Statistical Methods For Resolving Intratumor Heterogeneity With Single-Cell Dna Sequencing, Alexander Davis
Dissertations & Theses (Open Access)
Tumor cells have heterogeneous genotypes, which drives progression and treatment resistance. Such genetic intratumor heterogeneity plays a role in the process of clonal evolution that underlies tumor progression and treatment resistance. Single-cell DNA sequencing is a promising experimental method for studying intratumor heterogeneity, but brings unique statistical challenges in interpreting the resulting data. Researchers lack methods to determine whether sufficiently many cells have been sampled from a tumor. In addition, there are no proven computational methods for determining the ploidy of a cell, a necessary step in the determination of copy number. In this work, software for calculating probabilities from …
Modeling Gene Expression With Differential Equations, Madison Kuduk
Modeling Gene Expression With Differential Equations, Madison Kuduk
Capstone Showcase
Gene expression is the process by which the information stored in DNA is convertedinto a functional gene product, such as protein. The two main functions that makeup the process of gene expression are transcription and translation. Transcriptionand translation are controlled by the number of mRNA and protein in the cell. Geneexpression can be represented as a system of first order differential equations for the rateof change of mRNA and proteins. These equations involve transcription, translation,degradation and feedback loops. In this paper, I investigate a system of first orderdifferential equations to model gene expression proposed by Hunt, Laplace, Miller andPham in …
Recta: Regulon Identification Based On Comparative Genomics And Transcriptomics Analysis, Xin Chen, Anjun Ma, Adam Mcdermaid, Hanyuan Zhang, Chao Liu, Huansheng Cao, Qin Ma
Recta: Regulon Identification Based On Comparative Genomics And Transcriptomics Analysis, Xin Chen, Anjun Ma, Adam Mcdermaid, Hanyuan Zhang, Chao Liu, Huansheng Cao, Qin Ma
School of Computing: Faculty Publications
Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to …
General Equations For Natural Selection Under Complete Dominance, Kasthuri Kannan, Adriana Heguy
General Equations For Natural Selection Under Complete Dominance, Kasthuri Kannan, Adriana Heguy
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Evolution Of Mobile Promoters In Prokaryotic Genomes., Mahnaz Rabbani
Evolution Of Mobile Promoters In Prokaryotic Genomes., Mahnaz Rabbani
Electronic Thesis and Dissertation Repository
Mobile genetic elements are important factors in evolution, and greatly influence the structure of genomes, facilitating the development of new adaptive characteristics. The dynamics of these mobile elements can be described using various mathematical and statistical models. In this thesis, we focus on a specific category of mobile genetic elements, i.e. mobile promoters, which are mobile regions of DNA that initiate the transcription of genes. We present a class of mathematical models for the evolution of mobile promoters in prokaryotic genomes, based on data obtained from available sequenced genomes. Our novel location-based model incorporates two biologically meaningful regions of the …
Epistasis In Predator-Prey Relationships, Iuliia Inozemtseva
Epistasis In Predator-Prey Relationships, Iuliia Inozemtseva
Electronic Theses and Dissertations
Epistasis is the interaction between two or more genes to control a single phenotype. We model epistasis of the prey in a two-locus two-allele problem in a basic predator- prey relationship. The resulting model allows us to examine both population sizes as well as genotypic and phenotypic frequencies. In the context of several numerical examples, we show that if epistasis results in an undesirable or desirable phenotype in the prey by making the particular genotype more or less susceptible to the predator or dangerous to the predator, elimination of undesirable phenotypes and then genotypes occurs.