• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.691-708
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• 2014

This paper formalizes a special type of social networking knowledge, which is called "workflow performer-role affiliation networking knowledge." A workflow model specifies execution sequences of the associated activities and their affiliated relationships with roles, performers, invoked-applications, and relevant data. In Particular, these affiliated relationships exhibit a stream of organizational work-sharing knowledge and utilize business process intelligence to explore resources allotting and planning knowledge concealed in the corresponding workflow model. In this paper, we particularly focus on the performer-role affiliation relationships and their implications as organizational and business process intelligence in workflow-driven organizations. We elaborate a series of theoretical formalisms and practical implementation for modeling, discovering, and visualizing workflow performer-role affiliation networking knowledge, and practical details as workflow performer-role affiliation knowledge representation, discovery, and visualization techniques. These theoretical concepts and practical algorithms are based upon information control net methodology for formally describing workflow models, and the affiliated knowledge eventually represents the various degrees of involvements and participations between a group of performers and a group of roles in a corresponding workflow model. Finally, we summarily describe the implications of the proposed affiliation networking knowledge as business process intelligence, and how worthwhile it is in discovering and visualizing the knowledge in workflow-driven organizations and enterprises that produce massively parallel interactions and large-scaled operational data collections through deploying and enacting massively parallel and large-scale workflow models.

• Management Science and Financial Engineering
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• v.5 no.1
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• pp.27-49
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• 1999

WebME is an Web-based integrated modeling environment that implements a multi-facetted modeling approach to mathematical model representation and management. Key features of WebME include the following: (i) sharing of modeling knowledge on the Web, (ii) a user-friendly interface for creating, maintaining, and solving models, (iii) independent management of mathematical models from conceptual models, (iv) object-oriented conceptual blackboard concept, (v) multi-facetted mathematical modeling modeling, and (vi) declarative representation of mathematical knowledge. This paper presents details of design and implementation issues that were encountered in the development of WebME.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.683-690
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• 2004

Garment modeling and simulation is now one of the important elements in broad range of digital contents. Though there have been recent products on garment simulation, general users do not know well enough how to design a virtual costume that meets some requirements about its specific clothing pattern. In particular, Hanbok - the Korean traditional costume - has many different characteristics against western ones in the aspect of its pattern design and of draping. This paper presents a knowledge-driven approach for virtual Hanbok modeling without knowing how to make real Hanbok. First, parameterized knowledge for several fabric types art solicited using visual similarity assessment from simulated and real cloth. Secondly, based on the analysis of designer's knowledge, we defined multi-level adjustment processes of Hanbok measurements with regard to body shape features for different virtual actors. An experimental system is developed as the form of a Maya plug-in and the result shows the applicability of the proposed method.

• Journal of The Korean Association For Science Education
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• v.40 no.2
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• pp.127-140
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• 2020

This study investigated the influence of an explicit and reflective modeling instruction on the metamodeling knowledge of fourth-graders. Two fourth-grade classes in an elementary school in Seoul were selected and each class was assigned to an experimental group and a control group, respectively. The experimental group was engaged in explicit and reflective modeling instruction, whereas the control group was engaged in implicit modeling instruction. The two groups were surveyed before and after instruction on the basis of five metamodeling knowledge categories: definition, purpose, design/construction, changeability, and multiplicity. The experimental group showed positive changes in model's meaning, examples, purpose, changeability as well as multiplicity. In contrast, fewer students in the control group understood the meaning of the model and modeling. They also showed limited changes in their understandings with regards to the modeling instruction, and could not expand their understanding of the nature of model and modeling. The findings indicate that an explicit and reflective modeling instruction has positive influence on elementary students' metamodeling knowledge.

• Knowledge Management Research
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• v.11 no.3
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• pp.47-58
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• 2010

Knowledge is fundamental asset for firms in the contemporary economy. Organizations are attempting to leverage their knowledge resources by employing knowledge management. However, a large number of KM initiatives fail due to the ignoring of human factors. We adopt theoretical framework and augment it with extrinsic variables, individual, organizational, and systematic factors that are believed to influence knowledge sharing and outcome of travel agency.

• Korean Journal of Computational Design and Engineering
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• v.11 no.1
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• pp.57-66
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• 2006

Modeling product configurations needs large amounts of knowledge about technical and marketing restrictions on the product. Previous attempts to automate product configurations concentrate on representations and management of the knowledge for specific domains in fixed and isolated computing environments. Since the knowledge about product configurations is subject to continuous change and hard to express, these attempts often failed to efficiently manage and exchange the knowledge in collaborative product development. In this paper, XML Topic Map (XTM) is introduced to represent and exchange the knowledge about product configurations in collaborative product development. A product configuration model based on XTM along with its merger and inference facilities enables configuration engineers In collaborative product development to manage and exchange their knowledge efficiently. An implementation of the proposed product configuration model is presented to demonstrate that the proposed approach enables enterprises to exchange the knowledge about product configurations during their collaborative product development.

• Proceedings of the IEEK Conference
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• summer
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• pp.87-91
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• 2004

This research presents distributed database and knowledge-base modeling approach for intelligent product design system. The product design information in this study is described by a collection of rules and design knowledge that are utilized according to the product development procedures. In this work, a network-based architecture has been developed to enable dispersed designers to simultaneously accomplish remote design tasks. A client/server communication diagram has also been proposed to facilitate consistent primary information modeling for multi-user access and reuse of designed results. An intelligent product design system has been studied with the concepts of distributed database and network-based architecture in order to support concurrent engineering design and automatic design part assembly. The system provides the capability of composing new designs from proper design elements stored in the database and knowledge-base. The distributed intelligent product design is applied to the design of an automobile part as an example.

• Knowledge Management Research
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• v.20 no.1
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• pp.1-26
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• 2019

This tutorial introduces procedures and methods for performing structural equation modeling using R. For this, we present the whole process of analyzing the structural equations model from the confirmatory factor analysis to the path diagram generation using the lavaan package, which is relatively well evaluated among the R packages supporting the structural equation modeling, together with the R program codes. Considering that research applying structural equation modeling techniques is the mainstream in a variety of social sciences, including business administration, and that there is growing interest in open source R, this tutorial focuses on researchers who are looking for alternatives to traditional commercial statistical packages and is expected that it will be a useful guidebook for them.

• Knowledge Management Research
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• v.20 no.2
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• pp.1-24
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• 2019

This tutorial introduces procedures and methods for performing structural equation modeling using R. To do this, we present advanced analysis methods based on structural equation model such as mediation effect analysis, moderation effect analysis, moderated mediation effect analysis, and multiple-group analysis with R program code using R lavaan package that supports structural equation modeling. R is flexible and scalable, unlike traditional commercial statistical packages. Therefore, new analytical techniques are likely to be implemented ahead of any other statistical package. From this point of view, R will be a very appropriate choice for applying new analytical techniques or advanced techniques that researchers need. Considering that various studies in the social sciences are applying structural equations modeling techniques and increasing interest in open source R, this tutorial is expected to be useful for researchers who are looking for alternatives to existing commercial statistical packages.