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Comparison of Performance between Machine Learning Technique and Logistic Regression in Terms of Risk Prediction for Malignancy of the Intraductal Papillary Mucinous Neoplasm of Pancreas

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HPB 2021年 23卷 S51-S51页

作者： Kang, J.S. Lee, C. Han, Y. Choi, Y.J. Byun, Y. Kim, H. Kwon, W. Park, T. Jang, J.-Y. Surgery and Cancer Research Institute Seoul National University College of Medicine Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Republic of Korea

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Correction: Effects of polygenic risk score and sodium and potassium intake on hypertension in Asians: A nationwide prospective cohort study (July,10.1038/s41440-024-01784-7,2024)

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HYPERTENSION RESEARCH 2024年第11期47卷 3286-3286页

作者： Bae, Eunjin Ji, Yunmi Jo, Jinyeon Kim, Yaerim Lee, Jung Pyo Won, Sungho Lee, Jeonghwan Department of Internal Medicine Gyeongsang National University Changwon Hospital Changwon Republic of Korea Department of Internal Medicine College of Medicine Gyeongsang National University Jinju Republic of Korea Institute of Medical Science College of Medicine Gyeongsang National University Jinju Republic of Korea College of Natural Sciences Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea Department of Public Health Sciences Institute of Health & Environment Seoul National University Seoul Republic of Korea Interdisciplinary Program for Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea RexSoft Corps Seoul Republic of Korea Department of Internal Medicine College of Medicine Keimyung University School of Medicine Daegu Republic of Korea Department of Internal Medicine Seoul National University Boramae Medical Center Seoul Republic of Korea Department of Internal Medicine Seoul National University College of Medicine Seoul Republic of Korea

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An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

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BMC biomedical engineering 2019年第1期1卷 24页

作者： Hwejin Jung Bilal Lodhi Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea. Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea.

BACKGROUND:Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS:To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS:We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. O

关键词： Convolutional neural network Deep learning Histopathology Image analysis Nuclei segmentation

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Deep Hierarchical Embedding for Simultaneous Modeling of GPCR Proteins in a Unified Metric Space

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Research Square 2021年

作者： Lee, Taeheon Lee, Sangseon Kang, Minji Kim, Sun Looxid Labs Seoul06099 Korea Republic of BK21 FOUR Intelligence Computing Seoul National University Seoul08826 Korea Republic of Department of Computer Science Stanford University StanfordCA94305 United States Bioinformatics Institute Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately at each level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects. © 2021, CC BY.

关键词： Proteins

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Erratum to: DNA methylation and transcriptional noise

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Epigenetics & Chromatin 2014年第1期7卷 1-1页

作者： Iksoo Huh Taesung Park Jia Zeng Soojin V Yi Department of Statistics Bioinformatics and Biostatistics Laboratory Interdisciplinary Program in Bioinformatics Seoul National University Seoul 151-742 South Korea School of Biology Institute of Bioengineering and Biosciences Georgia Institute of Technology 310 Ferst Drive Atlanta GA 30332 USA

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Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

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SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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Corrigendum to “PlantMirnaT: miRNA and mRNA integrated analysis fully utilizing characteristics of plant sequencing data” [Methods 83 (2015) 80–87]

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Methods 2016年 92卷 94-94页

作者： S. Rhee H. Chae S. Kim Department of Computer Science and Engineering Seoul National University Seoul Republic of Korea School of Informatics and Computing Computer Science Department Indiana University Bloomington IN USA Bioinformatics Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea

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Titania Nanosheets Generates Peroxynitrite for S-Nitrosylation and Enhanced p53 Function in Lung Cancer Cells

Research Square

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Research Square 2021年

作者： Soonnarong, Rapeepun Tungsukruthai, Sucharat Nutho, Bodee Rungrotmongkol, Thanyada Vinayanuwattikun, Chanida Maluangnont, Tosapol Chanvorachote, Pithi Interdisciplinary Program of Pharmacology Graduate School Chulalongkorn University Bangkok Thailand Cell-based Drug and Health Products Development Research Unit Chulalongkorn University Bangkok Thailand Departments of Pharmacology and Physiology Faculty of Pharmaceutical Sciences Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Biocatalyst and Environmental Biotechnology Reseach Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Division of Medical Oncology Department of Medicine Chulalongkorn University Bangkok Thailand College of Materials Innovation and Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Background: Metal oxide nanomaterials are increasingly being exploited in cancer therapy thanks to their unique properties, which can enhance the efficacy of current cancer therapies. However, the nanotoxicity and mechanism of Ti0.8O2 nanosheets for specific site-targeting strategies in NSCLC have not yet been investigated. Methods: The effects of Ti0.8O2 nanosheets on cytotoxicity in NSCLC cells and normal cells were examined. The apoptosis characteristics, including condensed and fragmented nuclei, as assessed by positive staining with annexin V. The cellular uptake of the nanosheets and the induction of stress fiber were assessed via transmission electron microscopy (TEM) and scanning electron microscopy (SEM) analyses, respectively. We also evaluated the expression of protein in death mechanism to identify the molecular mechanisms behind the toxicity of these cells. We investigated the relationship between S-nitrosylation and the increase in p53 stability by molecular dynamics. Results: Ti0.8O2 nanosheets caused cytotoxicity in several lung cancer cells, but not in normal cells. The nanosheets could enter lung cancer cells and exert an apoptosis induction. Results for protein analysis further indicated the activation of p53, increased Bax, decreased Bcl-2 and Mcl-1, and activation of caspase-3. The nanosheets also exhibited a substantial apoptosis effect in drug-resistant metastatic primary lung cancer cells, and it was found that the potency of the nanosheets was dramatically higher than that of cisplatin and etoposide. In terms of their mechanism of action, we found that the mode of apoptosis induction was through the generation of cellular ONOO− mediated the S-nitrosylation of p53 at C182. Molecular dynamics analysis further showed that the S-nitrosylation of one C182 stabilized the p53 dimer. Consequently, this nitrosylation of the protein led to an upregulation of p53 through its stabilization. Conclusions: Taking all the evidence together, we provided info

关键词： Molecular dynamics

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Erratum to: Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

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Briefings in bioinformatics 2021年第1期22卷 607页

作者： Minsik Oh Sungjoon Park Sun Kim Heejoon Chae Department of Computer Science and Engineering Seoul National University Seoul 08826 Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul 08826 Korea. Bioinformatics Institute Seoul National University Seoul 08826 Korea. Division of Computer Science Sookmyung Women's University Seoul 04310 Korea.

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Erratum to: Discoidin Domain Receptor 1 is a therapeutic target for neurodegenerative diseases

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Human molecular genetics 2021年第13期30卷 1271-1272页

作者： Alan J Fowler Michaeline Hebron Kaluvu Balaraman Wangke Shi Alexander A Missner Jonathan D Greenzaid Timothy L Chiu Clementina Ullman Ethan Weatherdon Val Duka Yasar Torres-Yaghi Fernando L Pagan Xiaoguang Liu Habtom Ressom Jaeil Ahn Christian Wolf Charbel Moussa Department of Neurology Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC 20057 USA. Interdisciplinary Program in Neuroscience Georgetown University Medical Center Washington DC 20057 USA. Georgetown Howard Universities Center for Clinical and Translational Sciences Translational Biomedical Sciences Program Georgetown University Medical Center Washington DC 20057 USA. Department of Chemistry Georgetown University and Medicinal Chemistry Shared Resource Georgetown University Medical Center Washington DC 20057 USA. MedStar Georgetown University Hospital Movement Disorders Clinic Department of Neurology Washington DC 20057 USA. Department of Oncology Lombardi Comprehensive Cancer Center Georgetown University Medical Center Washington DC 20057 USA. Department of Bioinformatics Biostatistics and Biomathematics Georgetown University Medical Center Washington DC 20057 USA.

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