Engineering Commons^™

Latent Semantic Indexing Of Pubmed Abstracts For Identification Of Transcription Factor Candidates From Microarray Derived Gene Sets, Sujoy Roy, Kevin Heinrich, Vinhthuy Phan, Michael W. Berry, Ramin Homayouni

Faculty Publications and Other Works -- EECS

Background

Identification of transcription factors (TFs) responsible for modulation of differentially expressed genes is a key step in deducing gene regulatory pathways. Most current methods identify TFs by searching for presence of DNA binding motifs in the promoter regions of co-regulated genes. However, this strategy may not always be useful as presence of a motif does not necessarily imply a regulatory role. Conversely, motif presence may not be required for a TF to regulate a set of genes. Therefore, it is imperative to include functional (biochemical and molecular) associations, such as those found in the biomedical literature, into algorithms for …

Comparative Studies Of High-Throughput Biological Graphs, Jonathan Reyles, Charles Phillips

Faculty Publications and Other Works -- EECS

Background

The exponential growth of biological data has given rise to new and difficult challenges. Because large data is often dealt with, it is inefficient to infer from each individual characteristics of a given dataset. Bioinformaticists are developing quantitative techniques to analyze and interpret key data properties. Graph algorithms can provide powerful and intuitive insight on such properties [1]. Using this approach, we collect biological data from transcriptomic and protein-protein interaction (PPI) sources. These data can be represented as a correlation matrix, where the rows are the vertices and the columns are the edges. We will analyze these …

Decentralized Turbo Bayesian Ompressed Sensing With Application To Uwb Systems, Depeng Yang, Husheng Li, Gregory D. Peterson

Faculty Publications and Other Works -- EECS

In many situations, there exist plenty of spatial and temporal redundancies in original signals. Based on this observation, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is proposed to provide an efficient approach to transfer and incorporate this redundant information for joint sparse signal reconstruction. As a case study, the TBCS algorithm is applied in Ultra-Wideband (UWB) systems. A space-time TBCS structure is developed for exploiting and incorporating the spatial and temporal a priori information for space-time signal reconstruction. Simulation results demonstrate that the proposed TBCS algorithm achieves much better performance with only a few measurements in the presence of …