• Bulletin of the Korean Chemical Society
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• v.27 no.10
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• pp.1537-1541
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• 2006

The $^1H$ and $^{13}C$ chemical shifts of eleven flavone derivatives were completely determined by basic 1D and 2D NMR experiments. Nineteen flavone derivatives including the above eleven derivatives were examined for anti-oxidative effects using the 1,1-diphenyl-2-picryl-hydrazyl assay and Caenorhabditis elegans. In order to understand the relationships between the structures of flavone derivatives and their anti-oxidative activities, a Comparative Molecular Field Analysis was performed.

• 한국정보디스플레이학회:학술대회논문집
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• 2003.07a
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• pp.178-180
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• 2003

We performed semiempirical (AMl and ZINDO) and ab initio (HF and DFT) calculations, to investigate molecular structures and optical properties of DCM and its derivatives. DCM and its derivatives are used as a red fluorescent dopant of the organic electroluminescent host materials, $Alq_3$. We have studied the relationship between the molecular structure and the optical properties of these molecules for the improvement of EL efficiencies. Wavelength at the absorption maximum was found to be red-shifted when the molecule is substituted with both strong electron donating and withdrawing functional groups. A new red fluorescent dye was predicted by QSPR study based on calculations and experimental data.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1858-1874
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• 2021

Recent advances in the molecular and genetic characterization of central nervous system (CNS) tumors have ushered in a new era of tumor classification, diagnosis, and prognostic assessment. In this emerging and rapidly evolving molecular genetic era, imaging plays a critical role in the preoperative diagnosis and surgical planning, molecular marker prediction, targeted treatment planning, and post-therapy assessment of CNS tumors. This review provides an overview of the current imaging methods relevant to the molecular genetic classification of CNS tumors. Specifically, we focused on 1) the correlates between imaging features and specific molecular genetic markers and 2) the post-therapy imaging used for therapeutic assessment.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.32-32
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• 2010

Since the characteristics of plasmas depend strongly on the interactions between plasma particles such as electron, ions, and neutrals, a well-established atomic and molecular database is needed to understand and produce various types of plasma. Thus, National Fusion Research Institute (NFRI) started to establish the plasma property DB for fusion and industrial plasma from last 2002. Here we describe our recent data evaluation activities regarding to production of atomic and molecular data that are needed for modeling plasma in fusion tokamaks and also low temperature industrial plasmas.

• Genomics & Informatics
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• v.17 no.1
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• pp.3.1-3.4
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• 2019

Intratumor heterogeneity within a single tumor mass is one of the hallmarks of malignancy and has been reported in various tumor types. The molecular characterization of intratumor heterogeneity in breast cancer is a significant challenge for effective treatment. Using single-cell RNA sequencing (RNA-seq) data from a public resource, an ERBB pathway activated triple-negative cell population was identified. The differential expression of three subtyping marker genes (ERBB2, ESR1, and PGR) was not changed in the bulk RNA-seq data, but the single-cell transcriptomes showed intratumor heterogeneity. This result shows that ERBB signaling is activated using an indirect route and that the molecular subtype is changed on a single-cell level. Our data propose a different view on breast cancer subtypes, clarifying much confusion in this field and contributing to precision medicine.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.10
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• pp.4797-4802
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• 2016

Background: Breast cancer (BC) is the top cancer among women worldwide and has been the most frequent malignancy among Kazakhstan women over the past few decades. Information on clinical and histopathological features of metastatic breast cancer (MBC), as well as the distribution of molecular subtypes is limited for Kazakh people. Materials and Methods: The present observational retrospective study was carried out at Regional Oncologic Dispensaries in the North-East Region of Kazakhstan (in Semey and Pavlodar cities). Сlinical and histopathological data were obtained for a total of 570 MBC patients in the 10 year period from 2004-2013, for whom data on molecular subtype were available for 253. Data from hospital charts were entered into SPSS 20 for analysis by one-way ANOVA analysis of associations of different variables with 1-5 year survival. Pearson correlation and linear regression models were used to examine the relation between parameters with a p-value < 0.05 considered statistically significant. Results: No significant relationships were evident between molecular subtype and survival, site of metastases, stage or ethnicity. Young females below the age of 44 were slightly more likely to have triple negative lesions. While the ductal type greatly predomonated, luminal A and B cases had a higher percentage with lobular morphology. Conclusions: In this select group of metastatice brease cancer, no links were noted for survival with molecular subtype, in contrast to much of the literature.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• BMB Reports
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• v.35 no.4
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• pp.414-419
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• 2002

The genotype characteristic of cultivars was investigated, along with varieties of both of the jute species, Corchorus olitorius and Corchorus capsularis, in the germplasm collection at the Bangladesh Jute Research Institute (BJRI). DNA fingerprinting was generated for 9 different varieties and 12 accessions of jute cultivars by using random amplified polymorphic DNA(RAPD). A total of 29 arbitrary oligonucleotide primers were screened. Seven primers gave polymorphism within the varieties, and 6 primers detected polymorphism within the accessions that were tested. A dendrogram was engendered from these data, and this gave a distinct clustering of the cultivated species of jute. Therefore, we generated RAPD markers, which are species-specific. These primers can distinguish between C. olitorius and C. capsularis. From the dendrogram that we generated between the various members of these two species, we found the existing genetic classification that agrees with our molecular marking data. A different dendrogram showed that jute accessions could be clustered into three groups. These data will be invaluable in the conservation and utilization of the genetic pool in the germplasm collection.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.230-240
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• 2022

Drug regeneration technology is an efficient strategy than the existing new drug development process, which requires large costs and time by using drugs that have already been proven safe. In this study, we recognize the importance of the new drug regeneration aspect of new drug development and research in predicting functional similarities through the basic molecular structure that forms drugs. We test four string-based algorithms by using SMILES data and searching for their similarities. And by using the ATC codes, pair them with functional similarities, which we compare and validate to select the optimal model. We confirmed that the higher the molecular structure similarity, the higher the ATC code matching rate. We suggest the possibility of additional potency of random drugs, which can be predicted through data that give information on drugs with high molecular similarities. This model has the advantage of being a great combination with additional data, so we look forward to using this model in future research.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.