• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.331-348
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• 2019

Understanding the tumor heterogeneity due to differences in the growth pattern of metastatic tumors and rate of change is important for understanding the sensitivity of tumor cells to drugs and finding appropriate therapies. It is often possible to test for differences in population means using t-test or ANOVA when the group of N samples is distinct. However, these statistical methods can not be used unless the groups are distinguished as the data covered in this paper. Statistical methods have been studied to test heterogeneity between samples. The minimum combination t-test method is one of them. In this paper, we propose a maximum combinatorial t-test method that takes into account combinations that bisect data at different ratios. Also we propose a method based on the idea that examining the heterogeneity of a sample is equivalent to testing whether the number of optimal clusters is one in the cluster analysis. We verified that the proposed methods, maximum combination t-test method and gap statistic, have better type-I error and power than the previously proposed method based on simulation study and obtained the results through real data analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.803-808
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• 2023

The IoT industry is growing at a record growth rate every year, but developers face practical problems such as security, data storage, and heterogeneity between devices before developing an IoT platform. In particular, heterogeneity between devices occurs due to network type and protocol, and device firmware must be changed or multiple IoT platforms must be used in some cases. In addition, data is wasted due to redundant sensing due to the overflow of indiscriminate IoT devices. In this paper, we propose a device-independent firmware design to solve the heterogeneity between devices in the IoT platform environment where Local WAS uses the MQTT protocol.

• Health Policy and Management
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• v.20 no.1
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• pp.137-154
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• 2010

This study empirically analyzed the effects of the Top Management Team (TMT) on organizational performance. We verified whether the age heterogeneity, job heterogeneity (core career, core function and major), and process (communication and integration) of the TMT affect organizational performance (management performance and healthcare service quality evaluation level). We collected data about 473 members of the 2006 TMT in 81 medical institutions. We also utilized statistics of organizational performance from the Ministry for Health, Welfare and Family Affairs and the Korean Institute of Hospital Management. Results of the study showed that the age heterogeneity of TMT exerted a negative effect on the healthcare service quality evaluation level, while the process exerted a positive effect. However, the age heterogeneity, job heterogeneity, and process had no influence on management performance. We discussed the implications of such outcome of the investigation in comparison with the former studies on TMT and organizational performance, and presented its restrictions and future plans.

• Asia-Pacific Journal of Business
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• v.12 no.2
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• pp.39-60
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• 2021

Purpose - The purpose of this paper is exploring whether the characteristics and heterogeneity of the TMT play a moderating role in CSR and corporate value or not. Design/methodology/approach - The literature research method includes collecting, organizing, and analyzing the literature on the characteristics and heterogeneity of the TMT, the effect of corporate social responsibility (CSR), and corporate value. We analyze the contributions and limitations in existing research, grasp the current research status, and develop the research content of this article. The empirical analysis method is based on the data of Chinese A-share listed companies from 2001 to 2017. This allows us to study the moderating effect of the characteristics and heterogeneity of the TMT on CSR and corporate value. Findings - The TMT age, education degree, overseas background, and compensation have a positive moderating effect on CSR and corporate market value. The comprehensive heterogeneity of the TMT also has a positive effect on CSR and financial performance. Research implications or Originality - The research on the relationship between CSR and corporate value is still inconclusive. Some results have found a positive relationship, while others show a negative relationship. Studies exist that report mixed findings as well. This study has attempted to clarify this problem by adding potentially missing variables related on the TMT characteristics and heterogeneity, investigating causality effects.

• Molecules and Cells
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• v.42 no.3
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• pp.189-199
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• 2019

Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeostatically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.31-40
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• 2005

In this paper, we propose XMDR(XML Metadata Registry) to guarantee the interoperability of data in distributed database, and describe a data synchronizing agent system using it. The proposal of XMDR is to solve the data heterogeneity problem in the sharing and exchanging data. Data heterogeneity problem is generated by different definition or mismatching expression of the same information. Therefore, we define XMDR with XML document by analyzing data elements based on MDR specification. The proposed synchronizing agent system using XMDR not only solves data heterogeneity for data interoperability in synchronizing data but also provides more efficient the agent system by offering errors of low frequency in the number of systems and requests of synchronizing data.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.09a
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• pp.280-282
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• 2002

In analysis of pumping test data, generally infinite domain has been assumed. However, in many cases, this assumption was not readily satisfied. Some boundaries conditions and natural heterogeneity of hydrogeologic properties would play critical roles on groundwater flow and contaminant transport. This study examined effects of some boundary conditions and heterogeneity on the groundwater flow and contaminant transport with basic numerical groundwater modeling, which provides implications for remediation of contaminated groundwater.

• Genomics & Informatics
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• v.17 no.1
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• pp.2.1-2.12
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• 2019

Chronic obstructive pulmonary disease (COPD) is a type of progressive lung disease, featured by airflow obstruction. Recently, a comprehensive analysis of the transcriptome in lung tissue of COPD patients was performed, but the heterogeneity of the sample was not seriously considered in characterizing the mechanistic dysregulation of COPD. Here, we established a new transcriptome analysis pipeline using a deconvolution process to reduce the heterogeneity and clearly identified that these transcriptome data originated from the mild or moderate stage of COPD patients. Differentially expressed or co-expressed genes in the protein interaction subnetworks were linked with mitochondrial dysfunction and the immune response, as expected. Computational protein localization prediction revealed that 19 proteins showing changes in subcellular localization were mostly related to mitochondria, suggesting that mislocalization of mitochondria-targeting proteins plays an important role in COPD pathology. Our extensive evaluation of COPD transcriptome data could provide guidelines for analyzing heterogeneous gene expression profiles and classifying potential candidate genes that are responsible for the pathogenesis of COPD.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.165-178
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• 2000

Bayesian methods are considered for the multiple caputure-recapture data. Reference priors are developed for such model and sampling-based approach through Gibbs sampler is used for inference from posterior distributions. Furthermore approximate Bayes factors are obtained for model selection between trap and nontrap response models. Finally one methodology is implemented for a capture-recapture model in generated data and real data.