https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/238 2026-07-24T08:21:49Z 2026-07-24T08:21:49Z Nguyen, Tuan Hoang Anh Kim, Pok-Son Kutzner, Arne Heese, Klaus https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213261 2026-06-12T05:30:29Z 2026-12-01T00:00:00Z

Title: Cell cycle-regulated expression of Fam72a from the |Srgap2-Fam72a| master gene leads to Mis18a downregulation Authors: Nguyen, Tuan Hoang Anh; Kim, Pok-Son; Kutzner, Arne; Heese, Klaus Abstract: The novel |Srgap2–Fam72a| master gene, comprising SLIT-ROBO Rho GTPase-activating protein 2 (Srgap2) and family with sequence similarity 72 member A (Fam72a), has attracted attention for its potential role in regulating brain plasticity and supporting advanced cognitive functions in humans. Moreover, recent studies have identified Fam72a as a new cell cycle-regulated gene. In this study, we investigated the activity of the intergenic region (IGR) between the native Srgap2 and Fam72a gene pair and the signaling pathways of Fam72a upon mitogen epidermal growth factor (Egf) stimulation. We found that, under mitogen Egf stimulation, the IGR functions as a divergent promoter, simultaneously driving the transcription of Srgap2 and Fam72a in opposite directions. Furthermore, Fam72a downregulates MIS18 kinetochore protein A (Mis18a), a tightly cell cycle-regulated gene, and interferes with the RAC-alpha serine/threonine-protein kinase (Akt1) signaling pathway by downregulating phosphorylated Akt1 at Serine 473, thereby favoring the more direct mitogen activated protein kinase 1 (Mapk1) route to promote cellular proliferation. These findings provide insight into the role of Fam72a during the cell cycle and suggest that it may contribute to the proliferation of neural stem cells (NSCs).

2026-12-01T00:00:00Z Dash Sharma, Sidharth Sensharma, Sreemoyee Kutzner, Arne Fuhrer, Erwin Pramanik, Gopal Heese, Klaus Pramanik, Subrata https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217632 2026-06-26T05:30:40Z 2026-07-01T00:00:00Z

Title: Aberrant activation of FAM168B via chimeric PLEKHB2::FAM168B mRNA promotes breast invasive cancer progression Authors: Dash Sharma, Sidharth; Sensharma, Sreemoyee; Kutzner, Arne; Fuhrer, Erwin; Pramanik, Gopal; Heese, Klaus; Pramanik, Subrata Abstract: Fusion genes have emerged as pivotal oncogenic drivers across diverse cancer types. The predominant mechanisms underlying fusion gene formation include chromosomal aberrations and intergenic mRNA trans-splicing. With advances in cancer genomics and transcriptomics, the identification, prevalence, and functional characterization of fusion genes have become major areas of investigation. In this study, we performed a comprehensive analysis of fusion events involving the family with sequence similarity 168B (FAM168B) and pleckstrin homology domain–containing B2 (PLEKHB2) in breast invasive carcinoma (BRCA). Integrated DNA and RNA sequencing analyses revealed that the chimeric PLEKHB2::FAM168B mRNA transcript is generated through an intergenic mRNA trans-splicing mechanism. This chimeric transcript leads to elevated FAM168B protein expression via long intergenic non-coding RNA 02228 (LINC02228)–mediated activation of DEAD-box helicase 3 X-linked (DDX3X). This, in turn, activates the DAZ-associated protein 2 (DAZAP2)/homeodomain-interacting protein kinase 2 (HIPK2)/tumor protein 53 (TP53) signaling cascade, resulting in enhanced cell cycle progression and increased BRCA cell proliferation. In summary, our findings suggest that chimeric PLEKHB2::FAM168B mRNA may serve as a potential biomarker in BRCA.

2026-07-01T00:00:00Z Lee, Sun-Hyoung Roh, Taewoo Kim, Eunchan Yoon, Sang-Hyeak https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213263 2026-06-16T10:30:38Z 2026-06-01T00:00:00Z

Title: Designing educational chatbots in resource-constrained environments: A design science approach Authors: Lee, Sun-Hyoung; Roh, Taewoo; Kim, Eunchan; Yoon, Sang-Hyeak Abstract: Generative AI is expanding the role of educational chatbots in higher education, but many institutions still face financial, computational, and organizational constraints that limit the deployment of advanced systems. To address this challenge, this study uses a design science approach to develop and validate a constrained-environment educational chatbot design and operations framework for introductory human resource development (HRD) courses. The framework combines a resource-efficient small language model (SLM) with fine-tuning, retrieval-augmented generation, and prompt engineering, and is theoretically grounded in the process logics of Factory Physics, Lean, Agile, and Six Sigma to stabilize the learning flow, manage variation, and improve instructional responsiveness. The framework is instantiated through EduHRDBot and evaluated in an authentic university classroom setting for performance, efficiency, and learner experience. The findings show how design science can generate transferable design knowledge for building cost-efficient, pedagogically aligned educational chatbots in resource-constrained environments. Beyond a single-course implementation, the proposed framework offers a modular basis for broader human–AI co-learning ecosystems in higher education.

2026-06-01T00:00:00Z Liu, Linkai Lee, Ook https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/219161 2026-07-15T05:00:19Z 2026-06-01T00:00:00Z

Title: Semantic ID Based Tagging and Retrieval for Improved Job Matching Authors: Liu, Linkai; Lee, Ook Abstract: The job search system requires the assignment of user queries to structured tags such as occupations, skills, and industries. Although large language models assist in understanding user intent, open generation may violate established patterns or deviate from the initial query. We propose a unified Semantic ID space that amalgamates search queries with ESCO terminology inside a distinct lexicon. This space is constructed via a domain-oriented encoder and adopted by all modules: the labeler selects only from valid candidates; the rewriter follows simple constraints to preserve core meaning while increasing hit rate; and the ranker uses simple signals from the same code. We evaluate our approach using a public corpus comprising ESCO data, job advertisements and anonymized CVs. The findings include increased annotation precision, reduced unusual outputs, enhanced memory with consistent purpose alignment, and ongoing enhancements in ranking quality and coverage. These results suggest that shared discrete spaces serve as a concise, reliable control layer for large models in the field of job search, thus forming the basis for future cross-task expansion.

2026-06-01T00:00:00Z