• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.41-46
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• 2006

When new weapons are introduced, the target points estimation is one of the important objectives in the flight test as well as the safe separation. The prediction methods help to design the flight test schedule. However, the incremental aerodynamic coefficients in the aircraft flow field so-called BSE are difficult to predict. Generally, the semiempirical methods such as the grid methods, IFM and Flow TGP using database are used for estimation of BSE. However, these methods are quasi-steady methods using static aerodynamic loads. Nowadays the time-accurate CFD method is often used to predict the store separation event. In the current process, the incremental aerodynamic coefficients in BSE regime are calculated directly, and the elimination of delta coefficients is checked simultaneously. This stage can be used for the initial condition of Flow TGP with freestream database. Two dimensional supersonic and subsonic store separation problems have been simulated and incremental coefficients are calculated. The results show the time when the store gets out of BSE region.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.52-57
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• 2007

The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1539-1546
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• 2016

Specific patterns of the hereditary breast and ovarian cancer (HBOC) syndrome are related to mutations in the BRCA1 gene. One hundred unrelated breast cancer patients were interviewed to obtain clinical symptoms and signs, pedigree and familial history of HBOC syndrome related cancer. Subsequently, data were calculated using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk prediction model. Patients with high score of BOADICEA were offered genetic testing. Eleven patients with high score of BOADICEA, 2 patients with low score of BOADICEA, 2 patient's family members and 15 controls underwent BRCA1 genetic testing. Mutation screening using PCR-HRM was carried out in 22 exons (41 amplicons) of BRCA1 gene. Sanger sequencing was subjected in all samples with aberrant graph. This study identified 10 variants in the BRCA1 gene, consisting of 6 missense mutations (c.1480C>A, c.2612C>T, c.2566T>C, c.3113A>G, c.3548 A>G, c.4837 A>G), 3 synonymous mutations (c.2082 C>T, c.2311 T>C and c.4308T>C) and one intronic mutation (c.134+35 G>T). All variants tend to be polymorphisms and unclassified variants. However, no known pathogenic mutations were found.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1385-1395
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• 2014

Economic traits of livestock are affected by environmental factors and genetic factors. In addition, it is not affected by one gene, but is affected by interaction of genes. We used a linear regression model in order to adjust environmental factors. And, in order to identify gene-gene interaction effect, we applied data mining techniques such as neural network, logistic regression, CART and C5.0 using five-SNPs (single nucleotide polymorphism) of FASN (fatty acid synthase). We divided total data into training (60%) and testing (40%) data, and applied the model which was designed by training data to testing data. By the comparison of prediction accuracy, C5.0 was identified as the best model. It were selected superior genotype using the decision tree.

• Toxicological Research
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• v.35 no.3
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• pp.257-270
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• 2019

Silver nanoparticles (AgNPs) have been widely used in a variety of applications in innovative development; consequently, people are more exposed to this particle. Growing concern about toxicity from AgNP exposure has attracted greater attention, while questions about nanosilver-responsive genes and consequences for human health remain unanswered. By considering early detection and prevention of nanotoxicology at the genetic level, this study aimed to identify 1) changes in gene expression levels that could be potential indicators for AgNP toxicity and 2) morphological phenotypes correlating to toxicity of HepG2 cells. To detect possible nanosilver-responsive genes in xenogenic targeted organs, a comprehensive systematic literature review of changes in gene expression in HepG2 cells after AgNP exposure and in silico method, connection up- and down-regulation expression analysis of microarrays (CU-DREAM), were performed. In addition, cells were extracted and processed for transmission electron microscopy to examine ultrastructural alterations. From the Gene Expression Omnibus (GEO) Series database, we selected genes that were up- and down-regulated in AgNPs, but not up- and down-regulated in silver ion exposed cells, as nanosilver-responsive genes. HepG2 cells in the AgNP-treated group showed distinct ultrastructural alterations. Our results suggested potential representative gene data after AgNPs exposure provide insight into assessment and prediction of toxicity from nanosilver exposure.

• Genomics & Informatics
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• v.20 no.2
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• pp.19.1-19.15
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• 2022

Alfalfa (Medicago sativa) is an important food and feed crop which rich in mineral sources. The WUSCHEL-related homeobox (WOX) gene family plays important roles in plant development and identification of putative gene families, their structure, and potential functions is a primary step for not only understanding the genetic mechanisms behind various biological process but also for genetic improvement. A variety of computational tools, including MAFFT, HMMER, hidden Markov models, Pfam, SMART, MEGA, ProtTest, BLASTn, and BRAD, among others, were used. We identified 34 MsWOX genes based on a systematic analysis of the alfalfa plant genome spread in eight chromosomes. This is an expansion of the gene family which we attribute to observed chromosomal duplications. Sequence alignment analysis revealed 61 conserved proteins containing a homeodomain. Phylogenetic study sung reveal five evolutionary clades with 15 motif distributions. Gene structure analysis reveals various exon, intron, and untranslated structures which are consistent in genes from similar clades. Functional analysis prediction of promoter regions reveals various transcription binding sites containing key growth, development, and stress-responsive transcription factor families such as MYB, ERF, AP2, and NAC which are spread across the genes. Most of the genes are predicted to be in the nucleus. Also, there are duplication events in some genes which explain the expansion of the family. The present research provides a clue on the potential roles of MsWOX family genes that will be useful for further understanding their functional roles in alfalfa plants.

• Mycobiology
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• v.50 no.4
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• pp.254-257
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• 2022

Wolfiporia cocos is a wood-decay brown rot fungus belonging to the family Polyporaceae. While the fungus grows, the sclerotium body of the strain, dubbed Bokryeong in Korean, is formed around the roots of conifer trees. The dried sclerotium has been widely used as a key component of many medicinal recipes in East Asia. Wolfiporia cocos strain KMCC03342 is the reference strain registered and maintained by the Korea Seed and Variety Service for commercial uses. Here, we present the first draft genome sequence of W. cocos KMCC03342 using a hybrid assembly technique combining both short- and long-read sequences. The genome has a total length of 55.5 Mb comprised of 343 contigs with N50 of 332 kb and 95.8% BUSCO completeness. The GC ratio was 52.2%. We predicted 14,296 protein-coding gene models based on ab initio gene prediction and evidence-based annotation procedure using RNAseq data. The annotated genome was predicted to have 19 terpene biosynthesis gene clusters, which was the same number as the previously sequenced W. cocos strain MD-104 genome but higher than Chinese W. cocos strains. The genome sequence and the predicted gene clusters allow us to study biosynthetic pathways for the active ingredients of W. cocos.

• Genomics & Informatics
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• v.21 no.2
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• pp.21.1-21.14
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• 2023

Fusobacterium nucleatum is a gram-negative bacteria associated with diverse infections like appendicitis and colorectal cancer. It mainly attacks the epithelial cells in the oral cavity and throat of the infected individual. It has a single circular genome of 2.7 Mb. Many proteins in F. nucleatum genome are listed as "Uncharacterized." Annotation of these proteins is crucial for obtaining new facts about the pathogen and deciphering the gene regulation, functions, and pathways along with discovery of novel target proteins. In the light of new genomic information, an armoury of bioinformatic tools were used for predicting the physicochemical parameters, domain and motif search, pattern search, and localization of the uncharacterized proteins. The programs such as receiver operating characteristics determine the efficacy of the databases that have been employed for prediction of different parameters at 83.6%. Functions were successfully assigned to 46 uncharacterized proteins which included enzymes, transporter proteins, membrane proteins, binding proteins, etc. Apart from the function prediction, the proteins were also subjected to string analysis to reveal the interacting partners. The annotated proteins were also put through homology-based structure prediction and modeling using Swiss PDB and Phyre2 servers. Two probable virulent factors were also identified which could be investigated further for potential drug-related studies. The assigning of functions to uncharacterized proteins has shown that some of these proteins are important for cell survival inside the host and can act as effective drug targets.