• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1749-1756
• /
• 2014

Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.

• Korean Journal of Remote Sensing
• /
• v.20 no.3
• /
• pp.175-188
• /
• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

• Korean Journal of Agricultural Science
• /
• v.45 no.4
• /
• pp.583-590
• /
• 2018

Genus Typha L. (Typhaceae; Cattail in common) is one of the hydrophytic plants found in semi-aquatic regions. About nine to 18 species of the genus exist all over the world. In Korea, the most commonly found cattail species are T. laxmanni and T. angustifolia. The aim of this study was to prepare a cDNA library and sequences and analyze expressed sequence tags (ESTs) from these species, T. laxmanni and T. angustifolia. In the case of T. laxmanni, we observed that 715 out of 742 ESTs had high quality sequences, whereas the remaining 27 ESTs were low quality sequences. In this study, we identified 77 contigs, 393 unassembled clones and 65.7% singletons. Furthermore, in the case of T. angustifolia, we recorded 992 high quality EST sequences, and by excluding 28 low quality sequences from among them, we retrieved 120 contigs, 348 unassembled clones and 48.9% singletons. The basic local alignment search tool (BLAST) and Kyoto encyclopedia of genes and genomes (KEGG) database results enabled us to identify the functional categories, i.e., molecular function (16.5%), biological process (22.2%) and cellular components (61.3%). In addition, between these two species, the no hits and anonymous genes were 4.2% and 11.7% and 6.2% and 11.2% in T. laxmanni and T. angustifolia, respectively, based on the BLAST results. The study concluded that they have certain species-specific genes. Hence, the results of this study on these two species could be a valuable resource for further studies.

• Genomics & Informatics
• /
• v.19 no.4
• /
• pp.42.1-42.17
• /
• 2021

Salivary gland carcinoma (SGC) is rare cancer, constituting 6% of neoplasms in the head and neck area. The most responsible genes and pathways involved in the pathology of this disorder have not been fully understood. We aimed to identify differentially expressed genes (DEGs), the most critical hub genes, transcription factors, signaling pathways, and biological processes (BPs) associated with the pathogenesis of primary SGC. The mRNA dataset GSE153283 in the Gene Expression Omnibus database was re-analyzed for determining DEGs in cancer tissue of patients with primary SGC compared to the adjacent normal tissue (adjusted p-value < 0.001; |Log2 fold change| > 1). A protein interaction map (PIM) was built, and the main modules within the network were identified and focused on the different pathways and BP analyses. The hub genes of PIM were discovered, and their associated gene regulatory network was built to determine the master regulators involved in the pathogenesis of primary SGC. A total of 137 genes were found to be differentially expressed in primary SGC. The most significant pathways and BPs that were deregulated in the primary disease condition were associated with the cell cycle and fibroblast proliferation procedures. TP53, EGF, FN1, NOTCH1, EZH2, COL1A1, SPP1, CDKN2A, WNT5A, PDGFRB, CCNB1, and H2AFX were demonstrated to be the most critical genes linked with the primary SGC. SPIB, FOXM1, and POLR2A significantly regulate all the hub genes. This study illustrated several hub genes and their master regulators that might be appropriate targets for the therapeutic aims of primary SGC.

• Genomics & Informatics
• /
• v.20 no.3
• /
• pp.27.1-27.13
• /
• 2022

Oral squamous cell carcinoma (OSCC) is the most prevalent head and neck malignancy, with frequent cervical lymph-node metastasis, leading to a poor prognosis in OSCC patients. The present study aimed to identify potential markers, including microRNAs (miRNAs) and genes, significantly involved in the etiology of early-stage OSCC. Additionally, the main OSCC's dysregulated Gene Ontology annotations and significant signaling pathways were identified. The dataset GSE45238 underwent multivariate statistical analysis in order to distinguish primary OSCC tissues from healthy oral epithelium. Differentially expressed miRNAs (DEMs) with the criteria of p-value < 0.001 and |Log2 fold change| > 1.585 were identified in the two groups, and subsequently, validated targets of DEMs were identified. A protein interaction map was constructed, hub genes were identified, significant modules within the network were illustrated, and significant pathways and biological processes associated with the clusters were demonstrated. Using the GEPI2 database, the hub genes' predictive function was assessed. Compared to the healthy controls, main OSCC had a total of 23 DEMs. In patients with head and neck squamous cell carcinoma (HNSCC), upregulation of CALM1, CYCS, THBS1, MYC, GATA6, and SPRED3 was strongly associated with a poor prognosis. In HNSCC patients, overexpression of PIK3R3, GIGYF1, and BCL2L11 was substantially correlated with a good prognosis. Besides, "proteoglycans in cancer" was the most significant pathway enriched in the primary OSCC. The present study results revealed more possible mechanisms mediating primary OSCC and may be useful in the prognosis of the patients with early-stage OSCC.

• Journal of Biomedical Engineering Research
• /
• v.43 no.6
• /
• pp.434-440
• /
• 2022

Liver transplantation is a critical used treatment method for patients with end-stage liver disease. The number of cases of living donor liver transplantation is increasing due to the imbalance in needs and supplies for brain-dead organ donation. As a result, the importance of the accuracy of the donor's suitability evaluation is also increasing rapidly. To measure the donor's liver volume accurately is the most important, that is absolutely necessary for the recipient's postoperative progress and the donor's safety. Therefore, we propose liver segmentation in abdominal CT images from pre-operation, POD 7, and POD 63 with a two-dimensional U-Net. In addition, we introduce an algorithm to measure the volume of the segmented liver and measure the hepatectomy rate and regeneration rate of pre-operation, POD 7, and POD 63. The performance for the learning model shows the best results in the images from pre-operation. Each dataset from pre-operation, POD 7, and POD 63 has the DSC of 94.55 ± 9.24%, 88.40 ± 18.01%, and 90.64 ± 14.35%. The mean of the measured liver volumes by trained model are 1423.44 ± 270.17 ml in pre-operation, 842.99 ± 190.95 ml in POD 7, and 1048.32 ± 201.02 ml in POD 63. The donor's hepatectomy rate is an average of 39.68 ± 13.06%, and the regeneration rate in POD 63 is an average of 14.78 ± 14.07%.

• Journal of Biomedical Engineering Research
• /
• v.44 no.3
• /
• pp.211-217
• /
• 2023

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

• Genomics & Informatics
• /
• v.21 no.1
• /
• pp.8.1-8.19
• /
• 2023

Cancer of the stomach is the second most frequent cancer-related death worldwide. The survival rate of patients with gastric cancer (GC) remains fragile. There is a requirement to discover biomarkers for prognosis approaches. Helicobacter pylori in the stomach is closely associated with the progression of GC. We identified the genes associated with poor/favorable prognosis in H. pylori-induced GC. Multivariate statistical analysis was applied on the Gene Expression Omnibus (GEO) dataset GSE54397 to identify differentially expressed miRNAs (DEMs) in gastric tissues with H. pylori-induced cancer compared with the H. pylori-positive with non-cancerous tissue. A protein interaction map (PIM) was built and subjected to DEMs targets. The enriched pathways and biological processes within the PIM were identified based on substantial clusters. Thereafter, the most critical genes in the PIM were illustrated, and their prognostic impact in GC was investigated. Considering p-value less than 0.01 and |Log2 fold change| as >1, five microRNAs demonstrated significant changes among the two groups. Gene functional analysis revealed that the ubiquitination system, neddylation pathway, and ciliary process are primarily involved in H. pylori-induced GC. Survival analysis illustrated that the overexpression of DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, and TXNIP was associated with poor prognosis, while increased MRPS5 expression was related to a favorable prognosis in GC patients. DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, TXNIP, and MRPS5 may be considered prognostic biomarkers for H. pylori-induced GC. However, experimental validation is necessary in the future.

• Journal of Biomedical Engineering Research
• /
• v.44 no.5
• /
• pp.346-353
• /
• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• Korean Journal of Ecology and Environment
• /
• v.43 no.2
• /
• pp.319-337
• /
• 2010

Major objective of this study was to determine interannual and seasonal water quality along with characteristics of longitudinal gradients along the reservoir axis of the riverine zone (Rz)-to-lacustrine zone (Lz). Water quality dataset of five years during 2003~2007 used here were obtained from Ministry of Environment, Korea and ten physical, chemical and biological parameters were analyzed in the study. Similarity analysis, based on moropho-hydrological variables of reservoir surface area, watershed area, total inflow, and outflow, showed that the reservoirs were categorized as three groups of large-dam reservoirs (Chungju Reservoir, Daecheong Reservoir and Soyang Reservoir), mid-size reservoirs (Andong Reservoir, Yongdam Reservoir, Juam Reservoir and Hapcheon Reservoir), and small-size reservoirs (Hoengseong Reservoir and Buan Reservoir). According to the data comparison of high-flow year (2003) vs. lowflow year (2005), dissolved oxygen (DO), pH, biological oxygen demand (BOD), suspended solids (SS), total nitrogen (TN), total phosphorus (TP), chlorophyll-a (CHL) and electrical conductivity (EC) declined along the longitudinal axis of Rz to Lz and water transparency, based on Secchi depth (SD), increased along the axis. These results indicate that transparency was a function of Values of pH, DO, SS, SD, and EC at each site were greater in the low-flow year (2005) than the high-flow year (2003), whereas values of BOD, COD, TN, TP and CHL were greater in the high-flow year (2003). When values of TN, TP, CHL and SD in nine reservoirs were compared in the three zones of Rz, Tz, and Lz, values of TN, TP and CHL declined along longitudinal gradients and SD showed the opposite due to the sedimentation processes from the water column. Values of TN were not statistically correlated with TP values. The empirical linear models of TP-CHL and CHL-SD showed significant (p<0.05, $R^2$>0.04). In the mid-size reservoirs, the variation of CHL was explained ($R^2$=0.2401, p<0.0001, n=239) by the variation of TP. The affinities in the correlation analysis of mid-size reservoirs were greater in the CHL-SD model than any other empirical models, and the CHL-SD model had an inverse relations. In the meantime, water quality variations was evidently greater in Daecheong Reservoir than two reservoirs of Andong Reservoir and Hoengseong Reservoir as a result of large differences of water quality by long distance among Rz, Tz and Lz.