• Molecules and Cells
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• v.45 no.5
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• pp.343-352
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• 2022

The advent of the assay for transposase-accessible chromatin using sequencing (ATAC-seq) has shown great potential as a leading method for analyzing the genome-wide profiling of chromatin accessibility. A comprehensive reference to the ATAC-seq dataset for disease progression is important for understanding the regulatory specificity caused by genetic or epigenetic changes. In this study, we present a genome-wide chromatin accessibility profile of 44 liver samples spanning the full histological spectrum of nonalcoholic fatty liver disease (NAFLD). We analyzed the ATAC-seq signal enrichment, fragment size distribution, and correlation coefficients according to the histological severity of NAFLD (healthy control vs steatosis vs fibrotic nonalcoholic steatohepatitis), demonstrating the high quality of the dataset. Consequently, 112,303 merged regions (genomic regions containing one or multiple overlapping peak regions) were identified. Additionally, we found differentially accessible regions (DARs) and performed transcription factor binding motif enrichment analysis and de novo motif analysis to determine new biomarker candidates. These data revealed the gene-regulatory interactions and noncoding factors that can affect NAFLD progression. In summary, our study provides a valuable resource for the human epigenome by applying an advanced approach to facilitate diagnosis and treatment by understanding the non-coding genome of NAFLD.

• Molecules and Cells
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• v.46 no.2
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• pp.106-119
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• 2023

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.355-362
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• 2007

Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.167-175
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• 2023

Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture estimation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.303-314
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• 2023

This study aimed to assess the practical feasibility of advanced deformable image registration (DIR) algorithms in radiotherapy by employing two distinct datasets. The first dataset included 14 4D lung CT scans and 31 head and neck CT scans. In the 4D lung CT dataset, we employed the DIR algorithm to register organs at risk and tumors based on respiratory phases. The second dataset comprised pre-, mid-, and post-treatment CT images of the head and neck region, along with organ at risk and tumor delineations. These images underwent registration using the DIR algorithm, and Dice similarity coefficients (DSCs) were compared. In the 4D lung CT dataset, registration accuracy was evaluated for the spinal cord, lung, lung nodules, esophagus, and tumors. The average DSCs for the non-learning-based SyN and NiftyReg algorithms were 0.92±0.07 and 0.88±0.09, respectively. Deep learning methods, namely Voxelmorph, Cyclemorph, and Transmorph, achieved average DSCs of 0.90±0.07, 0.91±0.04, and 0.89±0.05, respectively. For the head and neck CT dataset, the average DSCs for SyN and NiftyReg were 0.82±0.04 and 0.79±0.05, respectively, while Voxelmorph, Cyclemorph, and Transmorph showed average DSCs of 0.80±0.08, 0.78±0.11, and 0.78±0.09, respectively. Additionally, the deep learning DIR algorithms demonstrated faster transformation times compared to other models, including commercial and conventional mathematical algorithms (Voxelmorph: 0.36 sec/images, Cyclemorph: 0.3 sec/images, Transmorph: 5.1 sec/images, SyN: 140 sec/images, NiftyReg: 40.2 sec/images). In conclusion, this study highlights the varying clinical applicability of deep learning-based DIR methods in different anatomical regions. While challenges were encountered in head and neck CT registrations, 4D lung CT registrations exhibited favorable results, indicating the potential for clinical implementation. Further research and development in DIR algorithms tailored to specific anatomical regions are warranted to improve the overall clinical utility of these methods.

• Genomics & Informatics
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• v.6 no.4
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• pp.202-209
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• 2008

Biological pathways are known as collections of knowledge of certain biological processes. Although knowledge about a pathway is quite significant to further analysis, it covers only tiny portion of genes that exists. In this paper, we suggest a model to extend each individual pathway using a microarray expression data based on the known knowledge about the pathway. We take the Rosetta compendium dataset to extend pathways of Saccharomyces cerevisiae obtained from KEGG (Kyoto Encyclopedia of genes and genomes) database. Before applying our model, we verify the underlying assumption that microarray data reflect the interactive knowledge from pathway, and we evaluate our scoring system by introducing performance function. In the last step, we validate proposed candidates with the help of another type of biological information. We introduced a pathway extending model using its intrinsic structure and microarray expression data. The model provides the suitable candidate genes for each single biological pathway to extend it.

• Mycobiology
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• v.47 no.4
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• pp.408-414
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• 2019

Crinum asiaticum and Hymenocallis littoralis, commonly known as spider lilies are bulbous perennial and herbaceous plants that widely planted in Malaysia as ornamental. During 2015-2016, symptom of leaf blight was noticed on the hosts from several locations in Penang. The symptom appeared as irregular brown to reddish lesions surrounded by yellow halos. As the disease progressed, the infected leaves became blighted, dried, and fell off with the presence of black microsclerotia and pycnidia on the lesions parts. The present study was conducted to investigate the causal pathogen of leaf blight on C. asiaticum and H. littoralis. Based on morphological characteristics and DNA sequences of internal transcribed spacer (ITS) region and translation elongation factor 1-alpha (TEF1-α) gene, the causal pathogen was identified as Macrophomina phaseolina. Phylogenetic analysis of combined dataset of ITS and TEF1-α grouped the isolates studied with other isolates of M. phaseolina from GenBank. The grouping of the isolates was supported by 96% bootstrap value. Pathogenicity test proved the role of the fungus in causing leaf blight on both hosts.

• BMB Reports
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• v.47 no.6
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• pp.348-353
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• 2014

The speed of sound (SOS) value is an indicator of bone mineral density (BMD). Previous genome-wide association (GWA) studies have identified a number of genes, whose variations may affect BMD levels. However, their biological implications have been elusive. We re-analyzed the GWA study dataset for the SOS values in skeletal sites of 4,659 Korean women, using a gene-set analysis software, GSA-SNP. We identified 10 common representative GO terms, and 17 candidate genes between these two traits (PGS < 0.05). Implication of these GO terms and genes in the bone mechanism is well supported by the literature survey. Interestingly, the significance levels of some member genes were inversely related, in several gene-sets that were shared between two skeletal sites. This implies that biological process, rather than SNP or gene, is the substantial unit of genetic association for SOS in bone. In conclusion, our findings may provide new insights into the biological mechanisms for BMD.

• Animal Systematics, Evolution and Diversity
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• v.40 no.2
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• pp.124-129
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• 2024

The genus Parornix Spuler, 1910 is a small group within the subfamily Parornichinae belonging to the family Gracillariidae in Korea. The subfamily Parornichinae was recently established as a taxonomic category by Kawahara & Ohshima, 2017, based on the largest dataset in a phylogenetic study. In this study, we reviewed the Korean species of the genus Parornix. In total, 5 species were recognized from Korea. Among them, one species, Parornix loganella (Stainton, 1848) is recorded for the first time from Korea. Descriptions and illustrations of the adults and genitalia of them are provided herein.

• Journal of Ecology and Environment
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• v.46 no.4
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• pp.292-303
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• 2022

Background: Climate change significantly influences the geographical distribution of plant species worldwide. Selecting indicator species allows for better-informed and more effective ecosystem management in response to climate change. The Korean Peninsula is the northernmost distribution zone of warm temperate evergreen broad-leaved (WTEB) species in Northeast Asia. Considering the ecological value of these species, we evaluated the current distribution range and future suitable habitat for 13 WTEB tree species designated as climate-sensitive biological indicator species. Results: Up-to-date and accurate WTEB species distribution maps were constructed using herbarium specimens and citizen science data from the Korea Biodiversity Observation Network. Current northern limits for several species have shifted to higher latitudes compared to previous records. For example, the northern latitude limit for Stauntonia hexaphylla is higher (37° 02' N, Deokjeokdo archipelago) than that reported previously (36° 13' N). The minimum temperature of the coldest month (Bio6) is the major factor influencing species distribution. Under future climate change scenarios, suitable habitats are predicted to expand toward higher latitudes inland and along the western coastal areas. Conclusions: Our results support the suitability of WTEB trees as significant biological indicators of species' responses to warming. The findings also suggest the need for consistent monitoring of species distribution shifts. This study provides an important baseline dataset for future monitoring and management of indicator species' responses to changing climate conditions in South Korea.