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Articles 1 - 5 of 5
Full-Text Articles in Other Computer Sciences
Computational Methods For Predicting Protein-Protein Interactions And Binding Sites, Yiwei Li
Computational Methods For Predicting Protein-Protein Interactions And Binding Sites, Yiwei Li
Electronic Thesis and Dissertation Repository
Proteins are essential to organisms and participate in virtually every process within cells. Quite often, they keep the cells functioning by interacting with other proteins. This process is called protein-protein interaction (PPI). The bonding amino acid residues during the process of protein-protein interactions are called PPI binding sites. Identifying PPIs and PPI binding sites are fundamental problems in system biology.
Experimental methods for solving these two problems are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods.
We present DELPHI, a deep learning based program for PPI site prediction and SPRINT, an algorithmic …
Computational Modelling Of Human Transcriptional Regulation By An Information Theory-Based Approach, Ruipeng Lu
Computational Modelling Of Human Transcriptional Regulation By An Information Theory-Based Approach, Ruipeng Lu
Electronic Thesis and Dissertation Repository
ChIP-seq experiments can identify the genome-wide binding site motifs of a transcription factor (TF) and determine its sequence specificity. Multiple algorithms were developed to derive TF binding site (TFBS) motifs from ChIP-seq data, including the entropy minimization-based Bipad that can derive both contiguous and bipartite motifs. Prior studies applying these algorithms to ChIP-seq data only analyzed a small number of top peaks with the highest signal strengths, biasing their resultant position weight matrices (PWMs) towards consensus-like, strong binding sites; nor did they derive bipartite motifs, disabling the accurate modelling of binding behavior of dimeric TFs.
This thesis presents a novel …
Optimizing The Analysis Of Electroencephalographic Data By Dynamic Graphs, Mehrsasadat Golestaneh
Optimizing The Analysis Of Electroencephalographic Data By Dynamic Graphs, Mehrsasadat Golestaneh
Electronic Thesis and Dissertation Repository
The brain’s underlying functional connectivity has been recently studied using tools offered by graph theory and network theory. Although the primary research focus in this area has so far been mostly on static graphs, the complex and dynamic nature of the brain’s underlying mechanism has initiated the usage of dynamic graphs, providing groundwork for time sensi- tive and finer investigations. Studying the topological reconfiguration of these dynamic graphs is done by exploiting a pool of graph metrics, which describe the network’s characteristics at different scales. However, considering the vast amount of data generated by neuroimaging tools, heavy computation load and …
Oligonucleotide Design For Whole Genome Tiling Arrays, Qin Dong
Oligonucleotide Design For Whole Genome Tiling Arrays, Qin Dong
Electronic Thesis and Dissertation Repository
Oligonucleotides are short, single-stranded fragments of DNA or RNA, designed to readily bind with a unique part in the target sequence. They have many important applications including PCR (polymerase chain reaction) amplification, microarrays, or FISH (fluorescence in situ hybridization) probes. While traditional microarrays are commonly used for measuring gene expression levels by probing for sequences of known and predicted genes, high-density, whole genome tiling arrays probe intensively for sequences that are known to exist in a contiguous region. Current programs for designing oligonucleotides for tiling arrays are not able to produce results that are close to optimal since they allow …
Integrated Development And Parallelization Of Automated Dicentric Chromosome Identification Software To Expedite Biodosimetry Analysis, Yanxin Li
Electronic Thesis and Dissertation Repository
Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the …