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Computational approach to identify enzymes that are potential therapeutic candidates for psoriasis

Enzyme Research 2011年第1期2011卷 826784页

作者： Park, Daeui Jeong, Hyoung Oh Kim, Byoung-Chul Ha, Young Mi Young Chung, Hae Interdisciplinary Research Program of Bioinformatics and Longevity Science Pusan National University Kumjeong-Gu Busan 609-735 South Korea Molecular Inflammation Research Center for Aging Intervention (MRCA) College of Pharmacy Pusan National University Kumjeong-Gu Busan 609-735 South Korea

Psoriasis is well known as a chronic inflammatory dermatosis. The disease affects persons of all ages and is a burden worldwide. Psoriasis is associated with various diseases such as arthritis. The disease is characterized by well-demarcated lesions on the skin of the elbows and knees. Various genetic and environmental factors are related to the pathogenesis of psoriasis. In order to identify enzymes that are potential therapeutic targets for psoriasis, we utilized a computational approach, combining microarray analysis and protein interaction prediction. We found 6,437 genes (3,264 upregulated and 3,173 downregulated) that have significant differences in expression between regions with and without lesions in psoriasis patients. We identified potential candidates through protein-protein interaction predictions made using various protein interaction resources. By analyzing the hub protein of the networks with metrics such as degree and centrality, we detected 32 potential therapeutic candidates. After filtering these candidates through the ENZYME nomenclature database, we selected 5 enzymes: DNA helicase (RUVBL2), proteasome endopeptidase complex (PSMA2), nonspecific protein-tyrosine kinase (ZAP70), I-kappa-B kinase (IKBKE), and receptor protein-tyrosine kinase (EGFR). We adopted a computational approach to detect potential therapeutic targets;this approach may become an effective strategy for the discovery of new drug targets for psoriasis. © 2011 Daeui Park et al.

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DNA steganalysis using deep recurrent neural networks

arXiv

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arXiv 2017年

作者： Bae, Ho Lee, Byunghan Kwon, Sunyoung Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Electrical and Computer Engineering Seoul National University Seoul08826 Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Clova AI Research NAVER Corp. Seongnam13561 ASRI INMC Seoul National University Seoul08826

Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are vari-ous methods that exist in other domains to detect hidden messages in conventional covert channels. However, they have not been applied to DNA steganography. The current most common detection approaches, namely frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography because those methods de-pend on the distribution of the number of sequence characters. To address this limitation, we propose a general sequence learning-based DNA steganalysis framework. The proposed approach learns the intrinsic distribution of coding and non-coding sequences and detects hidden messages by exploiting distribution variations after hiding these messages. Using deep recurrent neural networks (RNNs), our framework identifies the distribution varia-tions by using the classification score to predict whether a sequence is to be a coding or non-coding sequence. We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework. According to our experimental results, our approach delivers a robust detection performance compared to other tools. Copyright © 2017, The Authors. All rights reserved.

关键词： Steganography

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Topological data analysis can extract subgroups with high incidence rates of Type 2 diabetes

Topological data analysis can extract subgroups with high in...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Hyung Sun Kim Changwoo Yi Yongkang Kim Uhnmee Park Woong Kook Hyuk Kim Taesung Park Interdisciplinary Program in bioinformatics Seoul National University The Research Institute of Basic Sciences College of Natural Sciences Seoul National University Department of Statistics Seoul National University Department of Bioscience and biotechnology University of Suwon Department of Mathematical Sciences and Institute for Mathematical Data Analytics Research Center Seoul National University Department of Mathematical Sciences Seoul National University

ISBN: (纸本)9781538654897;9781538654880

Type 2 diabetes (T2D) is now a rapidly increasing, worldwide scourge, and the identification of genetic contributors is vital. However, current analyses of multiple, disease-contributing factors, and their combined interactions, remains quite difficult, using traditional approaches. Topological data analysis (TDA) shows what shape a data set can have, facilitating clustering analysis, by determining which components are close to each other. Thus, TDA can generate a network, using single-nucleotide polymorphism (SNP) data, revealing the genetic relatedness of specific individuals, and can derive multiple ordered subgroups, from one with a low patient concentration, to one with a high patient concentration. Since it is widely accepted that T2D pathogenesis is affected by multiple genetic factors, we performed TDA on T2D data from the Korea Association REsource (KARE) project, a population-based, genome-wide association study of the Korean adult population. Since KARE data contains follow-up information about the incidence of T2D, we compared the T2D status of each individual, at baseline, with that of 10 years later. For the TDA network-driven subgroups, ordered by prevalence, we compared the T2D incidence rate, after 10 years, for individuals initially without T2D. As a result, we found that the TDA network-driven, ordered subgroups had significantly increased incidence rates, linearly correlated with prevalence (p-value = 0.006914). Our results demonstrate the usefulness of TDA in both identifying genetic contributors (e.g., SNPs), and their interrelationships, in the pathology of complex diseases.

关键词： Data analysis Genetics bioinformatics Diabetes Diseases Conferences Data mining

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Integrated analysis of circRNA regulation with ADARB2 enrichment in inhibitory neurons

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Computers in Biology and Medicine 2024年 182卷 109212-109212页

作者： Yu, Suwan Ha, Hongseok Kim, Kwangsoo Interdisciplinary Program in Bioinformatics College of Natural Sciences Seoul National University Seoul Korea Republic of Institute of Endemic Disease Seoul National University Medical Research Center Seoul National University Seoul Korea Republic of Department of Medicine College of Medicine Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Institute of Convergence Medicine with Innovative Technology Seoul National University Hospital Seoul Korea Republic of

This study investigates the regulation of circular RNAs (circRNAs) with Adenosine Deaminase RNA Specific B2 (ADARB2) enrichment specifically in inhibitory neurons. Using an integrated analysis combining high-throughput sequencing and bioinformatics approaches, we identified a group of circRNAs that are potentially enhanced by ADARB2. Our findings highlight the pivotal role of ADARB2 in circRNA synthesis within inhibitory neurons, likely through its specific binding to precursor RNAs, which facilitates circRNA biogenesis. © 2024

关键词： Neurons

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IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarch...

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IEEE International Conference on Big Data

作者： Hongryul Ahn Inuk Jung Heejoon Chae Minsik Oh Inyoung Kim Sun Kim Division of Data Science DS&ML Center The University of Suwon Hwaseong Republic of Korea School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea BK21 Four Intelligence Computing Seoul National University Seoul Republic of Korea Artificial Intelligence Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics and Bioinformatics Institute Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665445993

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

关键词： Costs Shape Clustering methods Conferences Big Data Cost function Minimization

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Elimination of senescent cells by β-galactosidase-targeted prodrug attenuates inflammation and restores physical function in aged mice

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细胞研究（英文版） 2020年第7期30卷 574-589页

作者： Yusheng Cai Huanhuan Zhou Yinhua Zhu Qi Sun Yin Ji Anqi Xue Yuting Wang Wenhan Chen Xiaojie Yu Longteng Wang Han Chen Cheng Li Tuoping Luo Hongkui Deng The MOE Key Laboratory of Cell Proliferation and Differentiation College of Life Sciences Peking-Tsinghua Center for Life Sciences and School of Basic Medical Sciences State Key Laboratory of Natural and Biomimetic Drugs Peking University Health Science Center Peking University Beijing 100191 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology & Biotechnology Peking University Shenzhen Graduate School Shenzhen Guangdong 518055 China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China Key Laboratory of Bioorganic Chemistry and Molecular Engineering Ministry of Education and Beijing National Laboratory for Molecular Science College of Chemistry and Molecular Engineering Peking University Beijing 100871 China School of Life Sciences Joint Graduate Program of Peking-Tsinghua-NIBS Peking University Beijing 100871 China School of Life Sciences Center for Bioinformatics Center for Statistical Science Peking University Beijing 100871 China

Cellular senescence,a persistent state of cell cycle arrest,accumulates in aged organisms,contributes to tissue dysfunction,and drives age-related *** clearance of senescent cells is expected to decrease chronic,low-grade inflammation and improve tissue repair capacity,thus attenuating the decline of physical function in aged ***,selective and effective clearance of senescent cells of different cell types has proven ***,we developed a prodrug strategy to design a new compound based on the increased activity of lysosomalβ-galactosidase (β-gal),a primary characteristic of senescent *** prodrug SSK1 is specifically activated by β-gal and eliminates mouse and human senescent cells independently of senescence inducers and cell *** aged mice,our compound effectively cleared senescent cells in different tissues,decreased the senescence-and age-associated gene signatures,attenuated low-grade local and systemic inflammation,and restored physical *** results demonstrate that lysosomalβ-gal can be effectively leveraged to selectively eliminate senescent cells,providing a novel strategy to develop anti-aging interventions.

关键词： Apoptosis Senescence

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A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers

A molecular evolutionary algorithm for learning hypernetwork...

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Congress on Evolutionary Computation

作者： Ji-Hoon Lee Bado Lee Joon Shik Kim Russell Deaton Byoung-Tak Zhang Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea School of Computer Science & Engineering Seoul National University Seoul South Korea Biointelligence Laboratory Seoul National University Seoul South Korea Department of Computer Science and Engineering University of Arkansas Fayetteville USA Biointelligence Laboratory School of Computer Science & Engineering Seoul National University Seoul South Korea

We describe a "molecular" evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation.

关键词： DNA Libraries Training Computers Evolutionary computation In vitro Estimation

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Ciwars: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

SSRN

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SSRN 2024年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech BlacksburgVA24060 United States Fralin Life Science Institute Virginia Tech BlacksburgVA24060 United States Department of Civil and Environmental Engineering Virginia Tech BlacksburgVA24060 United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech BlacksburgVA24060 United States

The rise of antibiotic resistance (AR) poses substantial threats to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate continuous AR monitoring through longitudinal metagenomic data are lacking. To address this, we introduce Cyberinfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive ARG and taxonomic profiling, along with detection of anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and demonstrates its potential for informing effective and timely interventions to mitigate the spread and transmission of AR. CIWARS is broadly applicable to longitudinal metagenomic data from any environment and thus could be a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use when it is needed. The web server is freely available at https://***/. © 2024, The Authors. All rights reserved.

关键词： Gene encoding

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Exploring the mutation tendency of a specific region in the virus taxonomic groups using data mining techniques

Exploring the mutation tendency of a specific region in the ...

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IEEE International Conference on Big Data

作者： Mikyung Je Myeongji Cho Yoon Jung Kim Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Division of Bio IT Honam National Institute of Biological Resources Mokpo Republic of Korea Lab. of Computational Virology & Viroinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665480468

The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses.

关键词： Proteins Big Data Data mining Polymers Viruses (medical)

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Characterization of Antioxidative Flavonoids in Wild and Cultivated Soybean by Ion-Filtering Strategy-Combined Lc-Hrmsn Analysis

SSRN

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SSRN 2024年

作者： No, Kyoung Tai Kim, Kwan Joong Cho, Min Hyung Lee, Sangwon Ha, Jang-Ho Chu, Sung-June Chung, Gyuhwa Kim, Dae-Ok Bioinformatics & Molecular Design Research Center Incheon21983 Korea Republic of Department of Biotechnology Chonnam National University Yeosu59626 Korea Republic of Graduate School of Biotechnology Kyung Hee University Yongin17104 Korea Republic of Interdisciplinary Graduate Program Department of Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Yonsei University Korea Republic of

To profiling flavonoids in wild soybean (WB;Glycine soja Sieb & Zucc.) and cultivated soybean (CB;Glycine max (L.) Merr.), liquid chromatography-high resolution mass spectrometry (LC-HRMS), antioxidant assays, and chemometrics method were used. Total 70 flavonoids were tentatively identified with ion filter strategies containing precursor ion list, mass defect filter, nitrogen rule filter, and diagnostic ion filter from 20,999 features in WB and CB. Semi-quantification result revealed that WB has 1.4-, 93.1-, 6.3-, and 1.9-fold higher contents in isoflavone, flavan, procyanidin, and flavone than that of CB. In antioxidant assays, WB showed 1.89- to 5.83-fold higher antioxidant capacities than CB in four antioxidant capacity assays. In orthogonal partial least squares discriminant analysis, WB and CB were clearly classified, and 31 characteristic flavonoids were identified by variable importance in projection. Furthermore, epicatechin and procyanidins of WB showed significantly positive correlation with antioxidant capacities by Pearson's correlation. © 2024, The Authors. All rights reserved.

关键词： Flavonoids

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