Yongwook Choi

Learn More
UNLABELLED We present a web server to predict the functional effect of single or multiple amino acid substitutions, insertions and deletions using the prediction tool PROVEAN. The server provides rapid analysis of protein variants from any organisms, and also supports high-throughput analysis for human and mouse variants at both the genomic and protein(More)
Information theory traditionally deals with " conventional data, " be it textual data, image, or video data. However, databases of various sorts have come into existence in recent years for storing " unconventional data " including biological data, social data, web data, topographical maps, and medical data. In compressing such data, one must consider two(More)
Traditionally, the performance of distributed algorithms has been measured in terms of time and message complexity. Message complexity concerns the number of messages transmitted over all the edges during the course of the algorithm. However, in energy-constraint radio or wireless networks (e.g., sensor networks), energy is a critical factor in measuring(More)
Recently we have developed a new algorithm, PROVEAN (<u>Pro</u>tein <u>V</u>ariation <u>E</u>ffect <u>An</u>alyzer), for predicting the functional effect of protein sequence variations, including single amino acid substitutions and small insertions and deletions [2]. The prediction is based on the change,(More)
The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which(More)
In a recently proposed graphical compression algorithm [1], the following tree arose in the course of the analysis. The root contains n balls that are consequently distributed between two subtrees according to a simple rule: In each step, all balls independently move down to the left subtree (say with probability p) or the right subtree (with probability(More)
We consider the problem of finding optimal description for general unlabeled graphs. Given a probability distribution on labeled graphs, we introduced in [4] a structural entropy as a lower bound for the lossless compression of such graphs. Specifically, we proved that the structural entropy for the Erd˝ os–Rényi random graph, in which edges are added with(More)