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- Ab Initio Protein Structure Prediction (1)
- Bioinformatics (1)
- Conformational Ensemble Generator (1)
- Disulfide Bonds Prediction (1)
- Energy Function (1)
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- Flexible Energy Function (1)
- Integrins, prostate cancer, protein expression, computer software (1)
- Intrinsically Disordered Protein (1)
- Intrinsically Disordered Proteins (1)
- Large-Scale Data Analysis (1)
- Machine Learning (1)
- Predictor Framework (1)
- Protein-Protein Interaction (1)
- Replicated incomplete data, EM, gene sets, Gibbs sampling, signaling pathways, simulated annealing. (1)
- Transcriptome, Gene Expression, Next-Generation Sequencing, RNA-seq Pipeline, SAMMate, SAM/BAM Format, Single-end/Paired-end (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra
Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra
University of New Orleans Theses and Dissertations
Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …
Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal
Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal
University of New Orleans Theses and Dissertations
Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that …
Pcaanalyser: A 2d-Image Analysis Based Module For Effective Determination Of Prostate Cancer Progression In 3d Culture, Md Tamjidul Hoque, Louisa C. E. Windus, Carrie J. Lovitt, Vicky M. Avery
Pcaanalyser: A 2d-Image Analysis Based Module For Effective Determination Of Prostate Cancer Progression In 3d Culture, Md Tamjidul Hoque, Louisa C. E. Windus, Carrie J. Lovitt, Vicky M. Avery
Computer Science Faculty Publications
Three-dimensional (3D) in vitro cell based assays for Prostate Cancer (PCa) research are rapidly becoming the preferred alternative to that of conventional 2D monolayer cultures. 3D assays more precisely mimic the microenvironment found in vivo, and thus are ideally suited to evaluate compounds and their suitability for progression in the drug discovery pipeline. To achieve the desired high throughput needed for most screening programs, automated quantification of 3D cultures is required. Towards this end, this paper reports on the development of a prototype analysis module for an automated high-content-analysis (HCA) system, which allows for accurate and fast investigation of …
Multivariate Models And Algorithms For Systems Biology, Lipi Rani Acharya
Multivariate Models And Algorithms For Systems Biology, Lipi Rani Acharya
University of New Orleans Theses and Dissertations
Rapid advances in high-throughput data acquisition technologies, such as microarraysand next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developingeffective computational methods for analyzing data generated from such platforms. In thisdissertation, we address some of these challenges. We divide our work into two parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete …
Computational Pipeline For Human Transcriptome Quantification Using Rna-Seq Data, Guorong Xu
Computational Pipeline For Human Transcriptome Quantification Using Rna-Seq Data, Guorong Xu
University of New Orleans Theses and Dissertations
The main theme of this thesis research is concerned with developing a computational pipeline for processing Next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate tens of millions of short reads for each DNA/RNA sample. The alignment of a large volume of short reads to a reference genome is a key step in NGS data analysis. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing useful information. In order to assist biomedical researchers to conveniently access essential information from NGS data files in SAM/BAM format, we have …