• 한국균학회소식:학술대회논문집
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• 2014.10a
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• pp.11-11
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• 2014

Fungal pathogens have huge impact on health and economic wellbeing of human by causing life-threatening mycoses in immune-compromised patients or by destroying crop plants. A key determinant of fungal pathogenesis is their ability to undergo developmental change in response to host or environmental factors. Genetic pathways that regulate such morphological transitions and adaptation are therefore extensively studied during the last few decades. Given that epigenetic as well as genetic components play pivotal roles in development of plants and mammals, contribution of microbial epigenetic counterparts to this morphogenetic process is intriguing yet nearly unappreciated question to date. To bridge this gap in our knowledge, we set out to investigate histone modifications among epigenetic mechanisms that possibly regulate fungal adaptation and processes involved in pathogenesis of a model plant pathogenic fungus, Magnaporthe oryzae. M. oryzae is a causal agent of rice blast disease, which destroys 10 to 30% of the rice crop annually. Since the rice is the staple food for more than half of human population, the disease is a major threat to global food security. In addition to the socioeconomic impact of the disease it causes, the fungus is genetically tractable and can undergo well-defined morphological transitions including asexual spore production and appressorium (a specialized infection structure) formation in vitro, making it a model to study fungal development and pathogenicity. For functional and comparative analysis of histone modifications, a web-based database (dbHiMo) was constructed to archive and analyze histone modifying enzymes from eukaryotic species whose genome sequences are available. Histone modifying enzymes were identified applying a search pipeline built upon profile hidden Markov model (HMM) to proteomes. The database incorporates 22,169 histone-modifying enzymes identified from 342 species including 214 fungal, 33 plants, and 77 metazoan species. The dbHiMo provides users with web-based personalized data browsing and analysis tools, supporting comparative and evolutionary genomics. Based on the database entries, functional analysis of genes encoding histone acetyltransferases and histone demethylases is under way. Here I provide examples of such analyses that show how histone acetylation and methylation is implicated in regulating important aspects of fungal pathogenesis. Current analysis of histone modifying enzymes will be followed by ChIP-Seq and RNA-seq experiments to pinpoint the genes that are controlled by particular histone modifications. We anticipate that our work will provide not only the significant advances in our understanding of epigenetic mechanisms operating in microbial eukaryotes but also basis to expand our perspective on regulation of development in fungal pathogens.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.