• Genomics & Informatics
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• v.8 no.4
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• pp.212-218
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• 2010

An accurate system for predicting the survival of patients with oral squamous cell carcinoma (OSCC) will be useful for selecting appropriate therapies. A nomogram for predicting survival was constructed from 96 patients with primary OSCC who underwent surgical resection between January 1994 and June 2003 at the Yonsei Dental Hospital in Seoul, Korea. We performed univariate and multivariate Cox regression to identify survival prognostic factors. For the early stage patients group, the nomogram was able to predict the 5 and 10 year survival from OSCC with a concordance index of 0.72. The total point assigned by the nomogram was a significant factor for predicting survival. This nomogram was able to accurately predict the survival after treatment of an individual patient with OSCC and may have practical utility for deciding adjuvant treatment.

• BMB Reports
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• v.37 no.1
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• pp.35-44
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• 2004

Recently produced information on post-translational modifications makes it possible to interpret their biological regulation with new insights. Various protein modifications finely tune the cellular functions of each protein. Understanding the relationship between post-translational modifications and functional changes ("post-translatomics") is another enormous project, not unlike the human genome project. Proteomics, combined with separation technology and mass spectrometry, makes it possible to dissect and characterize the individual parts of post-translational modifications and provide a systemic analysis. Systemic analysis of post-translational modifications in various signaling pathways has been applied to illustrate the kinetics of modifications. Availability will advance new technologies that improve sensitivity and peptide coverage. The progress of "post-translatomics", novel analytical technologies that are rapidly emerging, offer a great potential for determining the details of the modification sites.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.3
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• pp.309-314
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• 2002

Random Amplified Polymorphic DNA (RAPD) assay was conducted to identify polymorphic markers in Amrithmahal, Krishna Valley, Hallikar, Deoni, Khillari, Ongole and Malnad Gidda breeds of South Indian cattle using twenty six primers. Of the 93 RAPD markers obtained, 53 were present in all breeds, 22 were individual specific and 18 were polymorphic for different breeds. Dual purpose breeds viz., Krishna Valley and Ongole showed less genetic divergence between them as compared to their genetic divergence from draft breeds viz., Amrithmahal, Hallikar and Khillari. Malnad Gidda was found to be a distinctly different from others studied.

• Genomics & Informatics
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• v.1 no.1
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• Genomics & Informatics
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• v.5 no.2
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• pp.41-45
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• 2007

Pharmacogenomics is the study that examines how genetic variations affect the ways in which people respond to drugs. The ways people respond to drugs are complex traits that are influenced by many different genes. Pharmacogenomics intends to develop rational means of optimizing drug therapy, with respect to the patients' genotype, to maximize efficacy with minimal adverse drug reactions. Pharmacogenomics has the potential to revolutionize the practice of medicine, and promises to usher in an area of personalized medicine, in which drugs and drug combinations are optimized for each individual's unique genetic makeup. Indeed, pharmacogenomics is exploited as an essential step for target discovery and drug development in the pharmaceutical industry. The goal of the personalized medicine is to get the right dose of the right drug to the right patient at the right time. In this article, we will review the use of pharmacogenomics in drug discovery and development.

• Genomics & Informatics
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• v.14 no.1
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• pp.2-11
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• 2016

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

• Toxicological Research
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• v.31 no.2
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• pp.89-96
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• 2015

Atopic dermatitis is a chronic inflammatory skin condition including severe pruritus, xerosis, visible eczematous skin lesions that mainly begin early in life. Atopic dermatitis exerts a profound impact on the quality of life of patients and their families. The estimated lifetime prevalence of atopic dermatitis has increased 2~3 fold during over the past 30 years, especially in urban areas in industrialized countries, emphasizing the importance of life-style and environment in the pathogenesis of atopic diseases. While the interplay of individual genetic predisposition and environmental factors contribute to the development of atopic dermatitis, the recent increase in the prevalence of atopic dermatitis might be attributed to increased exposure to various environmental factors rather than alterations in human genome. In recent decades, there has been an increasing exposure to chemicals from a variety of sources. In this study, the effects of various environmental chemicals we face in everyday life - air pollutants, contact allergens and skin irritants, ingredients in cosmetics and personal care products, and food additives - on the prevalence and severity of atopic dermatitis are reviewed.

• Genomics & Informatics
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• v.5 no.3
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• pp.118-123
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• 2007

The current existing literature offers little guidance on how to decide which method to use to analyze one-channel microarray measurements when dealing with large, grouped samples. Most previous methods have focused on two-channel data;therefore they can not be easily applied to one-channel microarray data. Thus, a more reliable method is required to determine an appropriate combination of individual basic processing steps for a given dataset in order to improve the validity of one-channel expression data analysis. We address key issues in evaluating the effectiveness of basic statistical processing steps of microarray data that can affect the final outcome of gene expression analysis without focusingon the intrinsic data underlying biological interpretation.

• Journal of Genetic Medicine
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• v.12 no.1
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• pp.1-5
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• 2015

De novo variants (DNVs) can arise during parental germ cell formation, fertilization, and the processes of embryogenesis. It is estimated that each individual carries 60-100 such spontaneous variants in the genome, most of them benign. However, a number of recent studies suggested that DNVs contribute to the pathogenesis of a variety of human diseases. Applications of DNVs include aiding in clinical diagnosis and identifying disease-causing genetic factors in patients with atypical symptoms. Therefore, understanding the roles of DNVs in a trio, with healthy parents and an affected offspring, would be crucial in elucidating the genetic mechanism of disease pathogenesis in a personalized manner.

• Genomics & Informatics
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• v.2 no.4
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• pp.167-173
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• 2004

The main mechanism of evolution is that biological entities change, are selected, and reproduce. We propose a different concept in terms of the main agent or atom of evolution: in the biological world, not an individual object, but its interactive network is the fundamental unit of evolution. The interaction network is composed of interaction pairs of information objects that have order information. This indicates a paradigm shift from 3D biological objects to an abstract network of information entities as the primary agent of evolution. It forces us to change our views about how organisms evolve and therefore the methods we use to analyze evolution.