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Search Results: 1 - 10 of 25271 matches for " Lee Doheon "
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Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases
Kim Sangwoo,Lee Doheon
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-s3-s2
Abstract: Background Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately. Methods We set four tumor type classes composed of various tumor characteristics such as tissue origin, cellular environment, and metastatic ability. We conducted a set of comparisons among the four tumor classes followed by searching for genes that are consistently up or down regulated through the whole comparisons. Results We identified four sets of genes that are consistently differently expressed in the comparisons, each of which denotes one of four cellular characteristics respectively – liver tissue, colon tissue, liver viability and metastasis characteristics. We found that our candidate genes for tissue specificity are consistent with the TiGER database. And we also found that the metastasis candidate genes from our method were more consistent with the known biological background and independent from other noise features. Conclusion We suggested a new method for identifying metastasis related genes from a large-scale database. The proposed method attempts to minimize the influences from other factors except metastatic ability including tissue originality and tissue viability by confining the result of metastasis unrelated test combinations.
Mathematical modeling of translation initiation for the estimation of its efficiency to computationally design mRNA sequences with desired expression levels in prokaryotes
Dokyun Na, Sunjae Lee, Doheon Lee
BMC Systems Biology , 2010, DOI: 10.1186/1752-0509-4-71
Abstract: We herein propose a mathematical model that focuses on translation initiation, which is the rate-limiting step in translation. The model uses mRNA-folding dynamics and ribosome-binding dynamics to estimate translational efficiencies solely from mRNA sequence information. We confirmed the feasibility of our model using previously reported expression data on the MS2 coat protein. For further confirmation, we used our model to design 22 luxR mRNA sequences predicted to have diverse translation efficiencies ranging from 10-5 to 1. The expression levels of these sequences were measured in Escherichia coli and found to be highly correlated (R2 = 0.87) with their estimated translational efficiencies. Moreover, we used our computational method to successfully transform a low-expressing DsRed2 mRNA sequence into a high-expressing mRNA sequence by maximizing its translational efficiency through the modification of only eight nucleotides upstream of the start codon.We herein describe a mathematical model that uses mRNA sequence information to estimate translational efficiency. This model could be used to design best-fit mRNA sequences having a desired protein expression level, thereby facilitating protein over-production in biotechnology or the protein expression-level optimization necessary for the construction of robust networks in synthetic biology.The emerging research field of synthetic biology differs from conventional biotechnology in terms of its problem-solving strategies [1]. Synthetic biology uses the engineering paradigm of system design to build biological systems with novel functionalities that often do not exist in nature. Therefore, synthetic biology allows the rational design or redesign of living systems at a deep and complex level [2-4], allowing researchers to use existing biological knowledge to rationally and systematically tackle biological problems.When synthetic networks are designed, genetic regulation is considered at the level of transcription, whil
Intelligibility of Reverberant Speech with Amplification: Limitation of Speech Intelligibility Metrics, and a Preliminary Examination of an Alternative Approach  [PDF]
Doheon Lee, Eunju Gong, Densil Cabrera, Manuj Yadav, William L. Martens
Journal of Applied Mathematics and Physics (JAMP) , 2015, DOI: 10.4236/jamp.2015.32028

This study examines the effect of speech level on intelligibility in different reverberation conditions, and explores the potential of loudness-based reverberation parameters proposed by Lee et al. [J. Acoust. Soc. Am., 131(2), 1194-1205 (2012)] to explain the effect of speech level on intelligibility in various reverberation conditions. Listening experiments were performed with three speech levels (LAeq of 55 dB, 65 dB and 75 dB) and three reverberation conditions (T20 of 1.0 s, 1.9 s and 4.0 s), and subjects listened to speech stimuli through headphones. Collected subjective data were compared with two conventional speech intelligibility parameters (Speech Intelligibility Index and Speech Transmission Index) and two loudness-based reverberation parameters (EDTN and TN). Results reveal that the effect of speech level on intelligibility changes with a room’s reverberation conditions, and that increased level results in reduced intelligibility in highly reverberant conditions. EDTN and TN explain this finding better than do STI and SII, because they consider many psychoacoustic phenomena important for the modeling of the effect of speech level varying with reverberation.

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects
Lee Sejoon,Lee Kwang H,Song Min,Lee Doheon
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-s2-s2
Abstract: Background Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched. Methods We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone. Results The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner. Conclusions We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.
The association of Alu repeats with the generation of potential AU-rich elements (ARE) at 3' untranslated regions.
Hyeong Jun An, Doheon Lee, Kwang Hyung Lee, Jonghwa Bhak
BMC Genomics , 2004, DOI: 10.1186/1471-2164-5-97
Abstract: Interspersed in the human genome, Alu repeats occupy 5% of the 3' UTR of mRNA sequences. Alu has poly-adenine (poly-A) regions at its end, which lead to poly-thymine (poly-T) regions at the end of its complementary Alu. It has been found that AREs are present at the poly-T regions. From the 3' UTR of the NCBI's reference mRNA sequence database, we found nearly 40% (38.5%) of ARE (Class I) were associated with Alu sequences (Table 1) within one mismatch allowance in ARE sequences. Other ARE classes had statistically significant associations as well. This is far from a random occurrence given their limited quantity. At each ARE class, random distribution was simulated 1,000 times, and it was shown that there is a special relationship between ARE patterns and the Alu repeats.AREs are mediating sequence elements affecting the stabilization or degradation of mRNA at the 3' untranslated regions. However, AREs' mechanism and origins are unknown. We report that Alu is a source of ARE. We found that half of the longest AREs were derived from the poly-T regions of the complementary Alu.Varying more than ten-fold, messenger RNA degradation is essential for the regulation of gene expression [1,2]. Differential mRNA decay rates were determined by specific cis-acting sequences within mRNA. For example, the mRNA sequences of yeast, many mammalians, and other eukaryotes contain AU-rich elements or AREs at their 3' untranslated regions (UTR) [3,4]. For example, in yeast, AREs stimulated the shortening of poly adenine (poly A), and two kinds of degradation pathways followed. One is 5'-to-3' exonuclease access by removal of the 5' cap structure. The other is 3'-to-5' digestion by a complex of exonucleases called exosome [5,6]. Genes required for these steps have been identified in yeast and were found to be conserved among eukaryotes. Although the mechanisms of AREs enhanced mRNA degradation are unknown, several groups provided evidence that 3'-to-5' degradation by the exosome may be
Context-dependent transcriptional regulations between signal transduction pathways
Sohyun Hwang, Sangwoo Kim, Heesung Shin, Doheon Lee
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-19
Abstract: Applied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation.Our new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request.Signal transduction is the primary process by which cells coordinate their metabolism, proliferation, and cellular communication according to environmental signals such as hormones, nutrients, and other chemical stimuli. Cells sense environmental signals by receptor proteins which convert the signals into various responses through signal transduction that are dependent on cellular contexts such as signals, receptor proteins that cells possess, and intracellular machinery by which cells integrate and interpret the signals [1]. For example, the JAK-STAT signal transduction pathway, which provides one of the most direct routes from cell-surface receptors to a nucleus, is activated by more than 30 cytokines of soluble mediators in cell communication. The cellular responses are different according to their cytokines even though they are stimulated by the same JAK-STAT signal transduction pathway [1].As well as for various responses stimulated by signal transduction pathways or signaling pathways, recent articles have presented abundant evidence for inter-pathway cross-communication according to cellular contexts [2-4]. Cytokine signaling which is critical in immune system regulates functions of other signaling pathways either by transcription-mediated consequences of cytokine signaling or by transcription-independent mechanisms [2]. As an example of transcription-mediated mechanisms, interferon gamma activates signal transduction pat
Combining Neuroinformatics Databases for Multi-Level Analysis of Brain Disorders
Hasun Yu,Joon Bang,Yousang Jo,Doheon Lee
Interdisciplinary Bio Central , 2012,
Abstract: With the development of many methods of studying the brain, the field of neuroscience has generated large amounts of information obtained from various techniques: imaging techniques, electrophysiological techniques, techniques for analyzing brain connectivity, techniques for getting molecular information of the brain, etc. A plenty of neuroinformatics databases have been made for storing and sharing this useful information and those databases can be publicly accessed by researchers as needed. However, since there are too many neuroinformatics databases, it is difficult to find the appropriate database depending on the needs of researcher. Moreover, many researchers in neuroscience fields are unfamiliar with using neuroinformatics databases for their studies because data is too diverse for neuroscientists to handle this and there is little precedent for using neuroinformatics databases for their research. Therefore, in this article, we review databases in the field of neuroscience according to both their methods for obtaining data and their objectives to help researchers to use databases properly. We also introduce major neuroinformatics databases for each type of information. In addition, to show examples of novel uses of neuroinformatics databases, we represent several studies that combine neuroinformatics databases of different information types and discover new findings. Finally, we conclude our paper with the discussion of potential applications of neuroinformatics databases
Inferring Pathway Activity toward Precise Disease Classification
Eunjung Lee ,Han-Yu Chuang ,Jong-Won Kim,Trey Ideker ,Doheon Lee
PLOS Computational Biology , 2008, DOI: 10.1371/journal.pcbi.1000217
Abstract: The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.
Optogenetic Mimicry of the Transient Activation of Dopamine Neurons by Natural Reward Is Sufficient for Operant Reinforcement
Kyung Man Kim, Michael V. Baratta, Aimei Yang, Doheon Lee, Edward S. Boyden, Christopher D. Fiorillo
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0033612
Abstract: Activation of dopamine receptors in forebrain regions, for minutes or longer, is known to be sufficient for positive reinforcement of stimuli and actions. However, the firing rate of dopamine neurons is increased for only about 200 milliseconds following natural reward events that are better than expected, a response which has been described as a “reward prediction error” (RPE). Although RPE drives reinforcement learning (RL) in computational models, it has not been possible to directly test whether the transient dopamine signal actually drives RL. Here we have performed optical stimulation of genetically targeted ventral tegmental area (VTA) dopamine neurons expressing Channelrhodopsin-2 (ChR2) in mice. We mimicked the transient activation of dopamine neurons that occurs in response to natural reward by applying a light pulse of 200 ms in VTA. When a single light pulse followed each self-initiated nose poke, it was sufficient in itself to cause operant reinforcement. Furthermore, when optical stimulation was delivered in separate sessions according to a predetermined pattern, it increased locomotion and contralateral rotations, behaviors that are known to result from activation of dopamine neurons. All three of the optically induced operant and locomotor behaviors were tightly correlated with the number of VTA dopamine neurons that expressed ChR2, providing additional evidence that the behavioral responses were caused by activation of dopamine neurons. These results provide strong evidence that the transient activation of dopamine neurons provides a functional reward signal that drives learning, in support of RL theories of dopamine function.
bZIPDB : A database of regulatory information for human bZIP transcription factors
Taewoo Ryu, Juhyun Jung, Sunjae Lee, Ho Nam, Sun Hong, Jae Yoo, Dong-ki Lee, Doheon Lee
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-136
Abstract: We constructed a database, designated bZIPDB, containing information on 49 human bZIP TFs, by means of automated literature collection and manual curation. bZIPDB aims to provide public data required for deciphering the gene regulatory network of the human bZIP family, e.g., evaluation or reference information for the identification of regulatory modules. The resources provided by bZIPDB include (1) protein interaction data including direct binding, phosphorylation and functional associations between bZIP TFs and other cellular proteins, along with other types of interactions, (2) bZIP TF-target gene relationships, (3) the cellular network of bZIP TFs in particular cell lines, and (4) gene information and ontology. In the current version of the database, 721 protein interactions and 560 TF-target gene relationships are recorded. bZIPDB is annually updated for the newly discovered information.bZIPDB is a repository of detailed regulatory information for human bZIP TFs that is collected and processed from the literature, designed to facilitate analysis of this protein family. bZIPDB is available for public use at http://biosoft.kaist.ac.kr/bzipdb webcite.Transcription factors (TFs) are responsible for gene expression in every living organism. The bZIP family shares a basic region and a leucine zipper domain. Homo/hetero-dimerization between family members is possible through the leucine zipper domain, and the proteins bind target promoters via the basic amino acid-rich region [1]. The bZIP TFs play essential roles in several processes in eukaryotic cells, from early development to tumorigenesis. For example, JUN is an oncogene that affects diverse cellular processes including proliferation, differentiation and apoptosis [2], while CEBPA is a well-known regulator of hepatocyte and adipocyte development [3].With the assistance of high-throughput technology, such as microarray technology, several researchers have attempted to decipher the regulatory networks of bZIP TFs
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