Youngmi Hur
Yonsei University · Computer Science
About the Lab
Professor Youngmi Hur's research lab specializes in the intersection of biomedical sciences and advanced signal processing, with a focus on identifying bioactive compounds in traditional fermented foods—particularly kimchi—that exhibit anti-cancer properties. The lab also conducts cutting-edge research in wavelet theory and multiresolution signal representations, developing novel mathematical frameworks for efficient data compression and analysis in high-dimensional spaces. These computational methods are applied to biological data, such as copy number aberrations in cancer genomics, to identify key driver genes. The lab integrates experimental biology with mathematical modeling to uncover molecular mechanisms underlying disease and health.
Research Overview
Research Output Trend
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Selected Papers
15Effect of solvent extracts and juice supernatants from kimchis on the growth of various human cancer cells was studied, comparing with the actions on normal cells. Inhibitory effect of kimchi extracts on [³H] thymidine incorporation in cancer cells was also investigated. The methanol extract, hexane extract and methanol soluble fraction (MSF) of 3-week fermented kimchi did not have growth inhibitory effect on Ac2F rat normal liver cells at the concentrations of 0.5~2%. However, marked decrease i
The Laplacian pyramid (LP) is a multiresolution representation introduced originally for images, and it has been used in many applications. A major shortcoming of the LP representation is that it is oversampled. The dependency among the LP coefficients is studied in this paper. It is shown that whenever the LP compression filter is interpolatory, the redundancy in the LP coefficients can be removed effortlessly by merely discarding some of the LP coefficients. Furthermore, it turns out that the
BACKGROUND: Copy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to de
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> A new wavelet-based methodology for representing data on regular grids is introduced and studied. The main attraction of this new “Local Compression-Alignment-Modified- Prediction (L-CAMP)” methodology is in the way it scales with the spatial dimension, making it, thus, highly suitable for the representation of high dimensional data. The specific highlights of the L-CAMP methodology are three. First,
Inhibitory effects of the methanol extract, hexane extract, methanol soluble fraction (MSF) and juice from 3 weeks fermented kimchi on the tumor formation in sarcoma-180 cell transplanted mice were studied. Effects of the solvent extracts and juice of the kimchi on the levels of lipid peroxide, glutathione, and the enzyme activities of the liver were also investigated in normal and sarcoma-180 cell transplanted mice. At 32 days following transplantation, MSF reduced the tumor formation by 54% co
A multivariate biorthogonal wavelet system can be obtained from a pair of multivariate biorthogonal refinement masks in multiresolution analysis setup. Some multivariate refinement masks may be decomposed into lower dimensional refinement masks. Tensor product is a popular way to construct a decomposable multivariate refinement mask from lower dimensional refinement masks. We present an alternative method, which we call coset sum, for constructing multivariate refinement masks from univariate re
In this paper we present a new approach for constructing the wavelet filter bank. Our approach enables constructing nonseparable multidimensional non-redundant wavelet filter banks with FIR filters using the Quillen-Suslin Theorem for Laurent polynomials. Our construction method presents some advantages over the traditional methods of multidimensional wavelet filter bank design. First, it works for any spatial dimension and for any sampling matrix. Second, it does not require the initial lowpass
Laplacian pyramid--based Laurent polynomial (LP$^2$) matrices are generated by Laurent polynomial column vectors and have long been studied in connection with Laplacian pyramidal algorithms in signal processing. In this paper, we investigate when such matrices are scalable, that is, when right multiplication by Laurent polynomial diagonal matrices results in paraunitary matrices. The notion of scalability has recently been introduced in the context of finite frame theory and can be considered as
As constructing multi-D wavelets remains a challenging problem, we propose a new method called prime coset sum to construct multi-D wavelets. Our method provides a systematic way to construct multi-D non-separable wavelet filter banks from two 1-D low-pass filters, with one of which being interpolatory. Our method has many important features including the following: 1) it works for any spatial dimension, and any prime scalar dilation; 2) the vanishing moments of the multi-D wavelet filter banks
We introduce new methodologies for the construction of high-performance very local Riesz wavelet bases of $L_2({\mathbb{R}^n})$ in arbitrarily high spatial dimension $n$. The localness $L$ of the representation is measured as the sum of the volumes of the supports of the underlying mother wavelets; small localness number is one of the sought-for properties in wavelet constructions. Our constructs are very simple and they are based on our recent framelet construction methods: the CAMP scheme and
Seedlessness is one of the most prized traits in table or raisin grapes. Seedlessness of grape derived from stenospermocarpy is thought to be controlled by one major dominant gene and multiple minor recessive genes. In the present study, we obtained dense variation data from an analysis of high-depth resequencing data of a diverse group of eight seeded, 18 seedless, and two wild grape genomes sequenced to &gt;41× mean depth. The genetic structure of the population and the relationships of th
Tight wavelet frames (TWFs) are computationally and theoretically attractive, but most existing multivariate constructions have various drawbacks, including low vanishing moments for the wavelets, or a large number of wavelet masks. We further develop existing work combining sums of squares representations with TWF construction, and present a new and general method for constructing such frames. Focusing on the case of box splines, we also demonstrate how the flexibility of our approach can lead
Research Areas
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