Byung-Jun Yoon

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In this paper we introduce an efficient algorithm for alignment of multiple large-scale biological networks. In this scheme, we first compute a probabilistic similarity measure between nodes that belong to different networks using a semi-Markov random walk model. The estimated probabilities are further enhanced by incorporating the local and the(More)
With the enormous amount of genomic and proteomic data that is available to us in the public domain, it is becoming increasingly important to be able to process this information in ways that are useful to humankind. Signal processing methods have played an important role in this context, some of which are reviewed in this paper. First we review the role of(More)
In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein-protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and(More)
With the advent of high-throughput technologies for measuring genome-wide expression profiles, a large number of methods have been proposed for discovering diagnostic markers that can accurately discriminate between different classes of a disease. However, factors such as the small sample size of typical clinical data, the inherent noise in high-throughput(More)
The problem of estimating a signal that is corrupted by additive noise has been of interest to many researchers for practical, as well as theoretical, reasons. Many of the traditional denoising methods use linear methods such as Wiener filtering. Recently, nonlinear methods, especially those based on wavelets, have become increasingly popular, due to a(More)
Accurate tools for multiple sequence alignment (MSA) are essential for comparative studies of the function and structure of biological sequences. However, it is very challenging to develop a computationally efficient algorithm that can consistently predict accurate alignments for various types of sequence sets. In this article, we introduce PicXAA(More)
Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various(More)