Mingon Kang

Investigation of impact factors for various performances of passive UHF RFID system

In this paper, we present causes of variation in performance for passive UHF RFID tags with empirical results in two different environments: practical conditions and an anechoic chamber. We study the critical causes of RSSI ambiguity, such as a posture of tag, and variations among uniform tags. Moreover, in passive UHF RFID systems, Tag-to-Tag interferences affect performance of passive RF tags. When two tags are located close each other, an adjacent tag influences the other tags. This causes increase or decrease of the backscattering communication budgets. According to our empirical results, compared with non-interfered backscattering signal strength in an anechoic chamber, tag-to-tag interference affects the reader received signal strength, such as 5.8dB of excess decrease and 2.5dB of increase, depending on the distance between two tags. We present a new model of backscattered signal strength for passive UHF RFID system under tag-to-tag interference. The variation of excess power volume by the interference depends on an interference coefficient. In order to analyze the impacts of tag-to-tag interference, we show empirical results in the anechoic chamber, then, we model the change of the backscattered signal strength using the second order under-damped system for different tag-to-tag distances and angles.

Multiblock Discriminant Analysis for Integrative Genomic Study

Human diseases are abnormal medical conditions in which multiple biological components are complicatedly involved. Nevertheless, most contributions of research have been made with a single type of genetic data such as Single Nucleotide Polymorphism (SNP) or Copy Number Variation (CNV). Furthermore, epigenetic modifications and transcriptional regulations have to be considered to fully exploit the knowledge of the complex human diseases as well as the genomic variants. We call the collection of the multiple heterogeneous data “multiblock data.” In this paper, we propose a novel Multiblock Discriminant Analysis (MultiDA) method that provides a new integrative genomic model for the multiblock analysis and an efficient algorithm for discriminant analysis. The integrative genomic model is built by exploiting the representative genomic data including SNP, CNV, DNA methylation, and gene expression. The efficient algorithm for the discriminant analysis identifies discriminative factors of the multiblock data. The discriminant analysis is essential to discover biomarkers in computational biology. The performance of the proposed MultiDA was assessed by intensive simulation experiments, where the outstanding performance comparing the related methods was reported. As a target application, we applied MultiDA to human brain data of psychiatric disorders. The findings and gene regulatory network derived from the experiment are discussed.

Sparse generalized canonical correlation analysis for biological model integration: A genetic study of psychiatric disorders

In the post-genomic era, unveiling causal traits in the complex mechanisms that involve a number of diseases has been highlighted as one of the key goals. Much research has recently suggested integrative approaches of both genomewide association studies (GWAS) and gene expression profiling-based studies provide greater insight of the mechanism than utilizing only one. In this paper, we propose a novel method, sparse generalized canonical correlation analysis (SGCCA), to integrate multiple biological data such as genetic markers, gene expressions, and disease phenotypes. The proposed method provides a powerful approach to comprehensively analyze complex biological mechanism while utilizing the multiple data simultaneously. The new method is also designed to identify a few of the elements significantly involved in the system among a large number of elements within the variable sets. The advantage of the method as well lies in the output of easily interpretable solutions. To verify the performance of SGCCA, we performed experiments with simulation data and human brain data of psychiatric diseases. Its capability to detect significant elements of the sets and the relations of the complex system is assessed.

Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization

Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Over the past decade, researchers reported a large number of human disease associations with the lncRNAs, both intergenic lncRNAs (lincRNAs) and non-intergenic lncRNAs. Thanks to the next generation sequencing platform, RNA-seq, through which researchers also were able to quantify expression profiles of each of the lncRNAs in human tissue samples. In this article we adapted the non-negative matrix factorization method to develop a low-rank computational model that can describe the existing knowledge about both non-intergenic and intergenic lncRNA-disease associations represented in a two dimensional association matrix as well as convey a way of ranking disease causing lncRNAs. We proposed several NMF formulations for the problem and we found that the sparsity-constrained NMF obtained the best model among all the other models. By exploiting the inherent bi-clustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance. Moreover, we proposed an integrative NMF formulation where we incorporated along with the coding gene and lincRNA disease association data, prior knowledge about relationship networks among the coding genes and lincRNAs, and the RNA-seq expression profile data to identify potential lincRNA-coding gene co-modules with which we further enhanced the lincRNA-disease associations and untangled mysteries about functional chemistry of the intergenic lncRNAs. Experimental results show the superiority of our proposed method over two state-of-the-art clustering algorithms—k-means and hierarchical clustering.

Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration

Developing vigorous mathematical models and estimating accurate parameters within feasible computational time are two indispensable parts to build reliable system models for representing biological properties of the system and for producing reliable simulation. For a complex biological system with limited observations, one of the daunting tasks is the large number of unknown parameters in the mathematical modeling whose values directly determine the performance of computational modeling. To tackle this problem, we have developed a data-driven global optimization method, nonlinear RANSAC, based on Random Sample Consensus (a.k.a. RANSAC) method, for parameter estimation of nonlinear system models. Conventional RANSAC method is sound and simple, but it is oriented for linear system models. We not only adopt the strengths of RANSAC, but also extend the method for nonlinear systems with outstanding performance. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. With well-defined mathematical nonlinear equations of the system, nonlinear RANSAC is performed for the parameter estimation. Moreover, simulations of the system for propagation prediction over the time are conducted under both normal conditions and knock-out conditions. In order to evaluate the general performance of the method, we also applied the method to signalling pathways where mathematical equations which are representing interaction of proteins are generated using ordinary differential equations as a general format, and public data sets for nonlinear regression evaluation are used to assess its performance.

Computational modeling of phagocyte transmigration during biomaterial-mediated foreign body responses

One of the major obstacles in computational modeling of a biological system is to determine a large number of parameters in the mathematical equations representing biological properties of the system. To tackle this problem, we have developed a global optimization method, called Discrete Selection Levenberg-Marquardt (DSLM), for parameter estimation. For fast computational convergence, DSLM suggests a new approach for the selection of optimal parameters in the discrete spaces, while other global optimization methods such as genetic algorithm and simulated annealing use heuristic approaches that do not guarantee the convergence. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. The goal of computational modeling is to construct an analyzer to understand the nature of the system. Also, the simulation by computational modeling for phagocyte transmigration provides critical clues to recognize current knowledge of the system and to predict yet-to-be observed biological phenomenon.

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

BackgroundInferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator.ResultsWe present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection.Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above.ConclusionsIn this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice

BackgroundComputational modeling and simulation play an important role in analyzing the behavior of complex biological systems in response to the implantation of biomedical devices. Quantitative computational modeling discloses the nature of foreign body responses. Such understanding will shed insight on the cause of foreign body responses, which will lead to improved biomaterial design and will reduce foreign body reactions. One of the major obstacles in computational modeling is to build a mathematical model that represents the biological system and to quantitatively define the model parameters.ResultsIn this paper, we considered quantitative inter connections and logical relationships among diverse proteins and cells, which have been reported in biological experiments and literature. Based on the established biological discovery, we have built a mathematical model while unveiling the key components that contribute to biomaterial-mediated inflammatory responses. For the parameter estimation of the mathematical model, we proposed a global optimization algorithm, called Discrete Selection Levenberg-Marquardt (DSLM). This is an extension of Levenberg-Marquardt (LM) algorithm which is a gradient-based local optimization algorithm. The proposed DSLM suggests a new approach for the selection of optimal parameters in the discrete space with fast computational convergence.ConclusionsThe computational modeling not only provides critical clues to recognize current knowledge of fibrosis development but also enables the prediction of yet-to-be observed biological phenomena.

Integration of DNA Methylation, Copy Number Variation, and Gene Expression for Gene Regulatory Network Inference and Application to Psychiatric Disorders

Biological network inference is a crucial problem to solve in Bioinformatics as most of biological process are based on bio molecular interactions. Many researchers have worked on especially the inference of gene regulatory networks where a node and edge represent a gene and regulation relationship respectively assuming that a gene can regulate another gene indirectly. However, a gene expression level can be influenced by not only genes and proteins but also other biological factors. Therefore, the inference could be more effective if those factors are considered in gene regulatory network inferences. In this paper, we propose an integrative approach to infer gene regulatory networks where a gene can be regulated by not only gene and but also DNA Methylation and copy number variation. It is assumed that a gene can be directly regulated by a single DNA Methylation and copy number variation at most. The simulation results show that our method outperforms popular and state-of-the-art methods of biological network inference. In addition, we applied the proposed method to psychiatric disorder data. The inferred networks provide the relationships within a set of genes that are more likely to be regulated by DNA Methylation and copy number variation of the genes.

Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection

Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.