• Genomics & Informatics
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• v.8 no.3
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• pp.150-158
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• 2010

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

• Journal of Biomedical Engineering Research
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• v.40 no.1
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• pp.1-6
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• 2019

Human mesenchymal stem cells (hMSC) are multipotent stromal cells that have great potential to differentiate into a variety of cell types such as osteocytes, chondrocytes, and myocytes. Although there have been many studies on their clinical availability, little is known about how intracellular signals can be modulated by topographic features of the extracellular matrix (ECM). In this study, we investigated whether and how microwavy-patterned extracellular matrix (ECM) could affect the signaling activity of focal adhesion kinase (FAK), a key cellular adhesion protein. The fluorescence resonance energy transfer (FRET)-based FAK biosensor-transfected cells are incubated on microwavy-patterned surfaces and then platelet derived growth factor (PDGF) are treated to trigger FAK signals, followed by monitoring through live-cell FRET imaging in real time. As a result, we report that PDGF-induced FAK was highly activated in cells cultured on microwavy-patterned surface with L or M type, while inhibited by H type-patterned surface. In further studies, PDGF-induced FAK signals are regulated by functional support of actin filaments, microtubules, myosin-related proteins, suggesting that PDGF-induced FAK signals in hMSC upon microwavy surfaces are dependent on cytoskeleton (CSK)-actomyosin networks. Thus, our findings not only provide new insight on molecular mechanisms on how FAK signals can be regulated by distinct topographical cues of the ECM, but also may offer advantages in potential applications for regenerative medicine and tissue engineering.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.197-205
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• 2002

The objectives of this Paper is to implement a diagnostic classifier of differential laryngeal diseases from acoustic signals acquired in a noisy room. For this Purpose, the voice signals of the vowel /a/ were collected from Patients in a soundproof chamber and got mixed with noise. Then, the acoustic Parameters were analyzed, and hierarchical neural networks were applied to the data classification. The classifier had a structure of five-step hierarchical neural networks. The first neural network classified the group into normal and benign or malign laryngeal disease cases. The second network classified the group into normal or benign laryngeal disease cases The following network distinguished polyp. nodule. Palsy from the benign laryngeal cases. Glottic cancer cases were discriminated into T1, T2. T3, T4 by the fourth and fifth networks All the neural networks were based on multilayer perceptron model which classified non-linear Patterns effectively and learned by an error back-propagation algorithm. We chose some acoustic Parameters for classification by investigating the distribution of laryngeal diseases and Pilot classification results of those Parameters derived from MDVP. The classifier was tested by using the chosen parameters to find the optimum ones. Then the networks were improved by including such Pre-Processing steps as linear and z-score transformation. Results showed that 90% of T1, 100% of T2-4 were correctly distinguished. On the other hand. 88.23% of vocal Polyps, 100% of normal cases. vocal nodules. and vocal cord Paralysis were classified from the data collected in a noisy room.

• Structural Engineering and Mechanics
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• v.11 no.5
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• pp.485-502
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• 2001

Modem soft computing methods, such as neural networks, evolutionary models and fuzzy logic, are mainly inspired by the problem solving strategies the biological systems use in nature. As such, the soft computing methods are fundamentally different from the conventional engineering problem solving methods, which are based on mathematics. In the author's opinion, these fundamental differences are the key to the full understanding of the soft computing methods and in the realization of their full potential in engineering applications. The main theme of this paper is to discuss the fundamental differences between the soft computing methods and the mathematically based conventional methods in engineering problems, and to explore the potential of soft computing methods in new ways of formulating and solving the otherwise intractable engineering problems. Inverse problems are identified as a class of particularly difficult engineering problems, and the special capabilities of the soft computing methods in inverse problems are discussed. Soft computing methods are especially suited for engineering design, which can be considered as a special class of inverse problems. Several examples from the research work of the author and his co-workers are presented and discussed to illustrate the main points raised in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1386-1392
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• 2006

This study is a numerical representative modelling analysis for applying the process that unravels networks between cells in genetics to WWW of informatics. Using the probabilistic graphical model, the insight from the data describing biological networks is used for making a probabilistic function. Rather than a complex network of cells, we reconstruct a simple lower-stage model and show a genetic representation level from the genetic based network logic. We made probabilistic graphical models from genetic data and extends them to genetic representation data in the method of network modelling in informatics.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.457-465
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• 2008

HL7(Health Level 7) is a standard for exchanging medical and healthcare data among different medical information systems. As the ubiquitous era is coming, in addition to text and imaging information, a new type of data, i.e., streaming sensor data appear. Since the HL7 is not covering the interfaces among the devices that produces sensor data, it is expected that sooner or later the HL7 needs to include the biomedical sensors and sensor networks. The IEEE 1451 is a family of standards that deals with the sensors, transducers including sensors and actuators, and various wired or wireless sensor networks. In this paper, we consider the possibility of interoperability between the IEEE 1451 and HL7. After we propose a format of messages in HL7 to include the IEEE 1451 TEDS, we present some preliminary results that show the possibility of integrating the two standards.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.285-289
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• 1997

In this paper, we describe the intrabed and interbed network in a developed patient monitoring system. Intrabed network handles data communication among the main unit of a bedside monitor and parameter modules plugged in it. Interbed network deals with a higher level data communication among many bedside monitors, central stations, DB servers, and clinical workstations. Analyzing the data communication requirements in each stage of the system, we designed the intrabed network based upon RS-485 and HDLC protocol with 1Mbps data rate. Interbed network is designed to utilize the industry standard 10Base-T Ethernet with TCP/IP and UDP protocol. We present the specifications and the performances of the developed data communication networks in the patient monitoring system.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1731-1735
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• 2013

Objective: We aimed to identify key genes, pathways and function modules in the development of diffuse large B-cell lymphoma (DLBCL) with microarray data and interaction network analysis. Methods: Microarray data sets for 7 DLBCL samples and 7 normal controls was downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified with Student's t-test. KEGG functional enrichment analysis was performed to uncover their biological functions. Three global networks were established for immune system, signaling molecules and interactions and cancer genes. The DEGs were compared with the networks to observe their distributions and determine important key genes, pathways and modules. Results: A total of 945 DEGs were obtained, 272 up-regulated and 673 down-regulated. KEGG analysis revealed that two groups of pathways were significantly enriched: immune function and signaling molecules and interactions. Following interaction network analysis further confirmed the association of DEGs in immune system, signaling molecules and interactions and cancer genes. Conclusions: Our study could systemically characterize gene expression changes in DLBCL with microarray technology. A range of key genes, pathways and function modules were revealed. Utility in diagnosis and treatment may be expected with further focused research.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.528-534
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• 2013

The energy consumption problem should be taken into consideration in wireless sensor network. Many studies have been proposed to address the energy consumption and delay problem. In this paper, we propose BISA(Bio-inspired Scheduling Algorithm) to reduce the energy consumption and delay in wireless sensor networks based on biological system. BISA investigates energy-efficient routing path and minimizes the energy consumption and delay using multi-channel for data transmission by multiplexing data transmission path. Through simulation, we confirm that the proposed scheme guarantees the efficient energy consumption and delay requirement.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.3
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• pp.332-338
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• 2009

Rapid extension of genomic database leads to the remarkable advance of functional genomics. This study proposes a novel methodology of functional analysis using 5-methyltrytophan (5 MT) mutant together with their 2-DE analysis and public microarray database. A total of 24 proteins was changed in 5 MT mutant and four remarkably different expressed proteins were identified. Among them, three spots were converted to Affymetrix probe. A total of 155 microarray samples from Gene Expression Omnibus (GEO) in NCBI was retrieved and followed by constructing gene co-expression networks over a broad range of biological issues through Self-Organising Tree Algorithm. Three co-expressing gene clusters were retrieved and each functional categorization with differential expression pattern was exhibited from 5 MT resistance mutant rice. It was indicated new co-expression networks in the mutant. This study suggests that on investigating possibility which correspond 2-DE to microarray database with their full potential.