• 한국정보통신학회논문지
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• 제18권7호
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• pp.1749-1756
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• 2014

단백질이 존재하는 세포내 위치에 대한 지식은 단백질의 기능과 관련된 중요한 정보이다. 본 논문은 개선된 레이블 멱집합 다중레이블 분류방법을 제안하여 단백질이 존재하는 세포내의 다중 위치를 예측한다. 다중레이블 분류 방법 중에서 레이블 멱집합 방법은 특정 생물학적 기능을 수행하는 단백질의 세포내 위치간의 연관 관계를 효과적으로 모델링할 수 있다. 본 논문은 다중레이블을 다른 다중레이블들의 선형조합으로 나타낼 때의 조합가중치를 제약조건이 있는 최적화를 통하여 구하고, 이를 사용하여 여러 다중레이블의 예측 확률들을 조합하여 최종적인 예측을 수행한다. 인간 단백질 자료에 대한 실험에서 제안한 방법이 다른 단백질 세포내 위치 예측 방법에 비하여 높은 성능을 보였다. 이는 제안한 방법이 레이블 멱집합 방법에서 사용되는 다중레이블들내에 존재하는 중복 정보를 이용하여 다중 레이블의 예측확률을 성공적으로 강화할 수 있기 때문이다.

• 대한원격탐사학회지
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• 제20권3호
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• pp.175-188
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• 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.

• 농업과학연구
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• 제45권4호
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• pp.583-590
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• 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
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• 제19권4호
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• pp.42.1-42.17
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• 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
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• 제20권3호
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• pp.27.1-27.13
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• 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.

• 대한의용생체공학회:의공학회지
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• 제43권6호
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• pp.434-440
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• 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%.

• 대한의용생체공학회:의공학회지
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• 제44권3호
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• pp.211-217
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• 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
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• 제21권1호
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• pp.8.1-8.19
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• 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.

• 대한의용생체공학회:의공학회지
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• 제44권5호
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• pp.346-353
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• 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.

• 생태와환경
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• 제43권2호
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• pp.319-337
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• 2010

본 연구는 우리나라 인공호들에서 연별 계절별 수질변이 특성 및 상 하류 간 수질의 종적구배 특성을 파악하기 위해 2003~2007년의 자료(물환경정보시스템)의 월별, 연별, 공간별(유수대, 전이대, 정수대)의 수질자료를 비교 평가하였다. 10개의 물리적, 화학적, 생물적 수질 변수를 이용하였으며, 인공호들의 일반 수질 특성, 수리수문학적 수질 특성, 시간적 수질변이 특성, 공간적 수질 변이 특성을 분석하였다. 저수면적, 유역면적, 유입량, 방류량을 기준으로 9개의 인공호에 대하여 유사도 분석을 실시하였으며, 그 결과 대형인공호(충주호, 대청호, 소양호), 중형인공호(안동호, 용담호, 주암호, 합천호), 소형인공호(횡성호, 부안호)로 크게 3개 그룹으로 나누어졌고, 유역 크기별로 비슷한 수질 양상을 나타냈다. 홍수의 해(2003년)와 가뭄의 해(2005년)로 대별하여 수질 자료를 분석한 결과, pH, DO, BOD, SS, TN, TP, CHL, EC는 Rz에서 Lz으로 갈수록 값이 감소하였고, SD는 LZ으로 갈수록 값이 증가하였다. 이러한 결과는 인공호내의 영양물질과 부유물질의 침강작용 및 광제한으로 인한 CHL의 감소가 SD 값의 증가에 영향을 미친 것으로 사료되었다. 각 지점별 pH, DO, SS, SD, EC는 가뭄의 해인 2005년에 컸고, BOD, COD, TN, TP, CHL은 홍수의 해인 2003년에 높게 나타났다. 공간적인 수질분포 특성을 분석하기 위하여 9개 인공호의 Rz, Tz, Lz에서의 TN, TP, CHL, SD의 수치를 비교하였다. 그 결과, TN, TP, CHL은 Rz에서 Lz으로 갈수록 침강작용에 의해 그 값이 감소하였고, SD는 반대 양상을 보였다. TN과 TP 사이에서 상관 관계를 분석한 결과, 두 수질 변수 사이의 상관성은 유의하지 않은 것으로 나타났다. 대형 인공호의 CHL-SD 모델에서 Rz, Lz의 경우를 제외하고, TP-CHL, CHL-SD는 유의한 상관성이 있는 것으로 나타났으며, TP-CHL의 상관관계는 중형인공호의 Rz ($R^2$=0.2401, p<0.0001, N=239)에서 유의한 상관성을 보이는 것으로 나타났다. CHL-SD의 상관관계에서도 중형인공호가 가장 높게 나타났으나 TP-CHL의 상관관계와는 반대되는 역상관 관계를 나타냈다. 대청호, 안동호, 횡성호를 Rz, Tz, Lz에서 수질자료를 분석한 결과, 대청호에서 수질자료의 값이 구간별로 큰 변이성을 보이는 것으로 나타났는데, 이는 대청호가 대형인공호로서 구간 간 거리가 멀고, 수심이 깊어 Rz, Tz, Lz의 구간 간 특성이 뚜렷하게 구분된 것으로 사료되었다.