Bibliografias temáticas / Disease association

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Literatura científica selecionada sobre o tema "Disease association"

Autor: Grafiati
Publicado: 4 de junho de 2021
Última modificação: 26 de julho de 2025

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Artigos de revistas sobre o assunto "Disease association"

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Yu, Dong-Ling, Zu-Guo Yu, Guo-Sheng Han, Jinyan Li, and Vo Anh. "Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction." Biomedicines 9, no. 9 (2021): 1152. http://dx.doi.org/10.3390/biomedicines9091152.

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Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It
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Shen, Zhen, You-Hua Zhang, Kyungsook Han, Asoke K. Nandi, Barry Honig, and De-Shuang Huang. "miRNA-Disease Association Prediction with Collaborative Matrix Factorization." Complexity 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/2498957.

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As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarit
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Suratanee, Apichat, and Kitiporn Plaimas. "DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations." Bioinformatics and Biology Insights 9 (January 2015): BBI.S35237. http://dx.doi.org/10.4137/bbi.s35237.

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Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease-disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random wa
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Yan, Cheng, Guihua Duan, Na Li, Lishen Zhang, Fang-Xiang Wu, and Jianxin Wang. "PDMDA: predicting deep-level miRNA–disease associations with graph neural networks and sequence features." Bioinformatics 38, no. 8 (2022): 2226–34. http://dx.doi.org/10.1093/bioinformatics/btac077.

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Abstract Motivation Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA–disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA–disease associations, and they cannot predict the deep-level miRNA–disease association types. Results In this study, we propose a new end-to-end deep learning method (calle
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Sarntivijai, Sirarat, Drashtti Vasant, Simon Jupp, et al. "Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation." Journal of Biomedical Semantics 7, no. 1 (2016): 8. https://doi.org/10.1186/s13326-016-0051-7.

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<strong>Background: </strong>The Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/ ) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights
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Hou, Jialu, Hang Wei, and Bin Liu. "iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network." PLOS Computational Biology 18, no. 10 (2022): e1010671. http://dx.doi.org/10.1371/journal.pcbi.1010671.

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Motivation Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation. Results With the growth of piRNA-disease association data, it is possible
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Opap, Kenneth, and Nicola Mulder. "Recent advances in predicting gene–disease associations." F1000Research 6 (April 26, 2017): 578. http://dx.doi.org/10.12688/f1000research.10788.1.

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Deciphering gene–disease association is a crucial step in designing therapeutic strategies against diseases. There are experimental methods for identifying gene–disease associations, such as genome-wide association studies and linkage analysis, but these can be expensive and time consuming. As a result, various in silico methods for predicting associations from these and other data have been developed using different approaches. In this article, we review some of the recent approaches to the computational prediction of gene–disease association. We look at recent advancements in algorithms, cat
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Zhou, Shunxian, Zhanwei Xuan, Lei Wang, Pengyao Ping, and Tingrui Pei. "A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network." Computational and Mathematical Methods in Medicine 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6789089.

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Motivation. Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of
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Fan, Ruizhi, Chenhua Dong, Hu Song, et al. "EHAI: Enhanced Human Microbe-Disease Association Identification." Current Protein & Peptide Science 21, no. 11 (2020): 1078–84. http://dx.doi.org/10.2174/1389203721666200702150249.

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: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association dis
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Turechek, W. W. "Nonparametric Tests in Plant Disease Epidemiology: Characterizing Disease Associations." Phytopathology® 94, no. 9 (2004): 1018–21. http://dx.doi.org/10.1094/phyto.2004.94.9.1018.

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Nonparametric tests are suited to many statistical applications, including experimental design, regression, and time series analysis, for example. Often these tests are thought of as alternatives to their parametric counterparts when certain assumptions about the underlying population are questionable. Although suited for this scenario, there are a number of nonparametric tests that fill unique niches in the analysis of data, for example, characterizing interspecific associations. Quantifying the degree of association between two or more pathogens or diseases at a defined spatial scale is esse
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Teses / dissertações sobre o assunto "Disease association"

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Ramzan, Naveen, Shimin Zheng, Hemang Panchal, Edward Leinaar, Christian Nwabueze, and Timir K. Paul. "Investigating The Association Between Chronic Kidney Disease and Clinical Outcomes." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/21.

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Background Chronic Kidney Disease (CKD) can be described as the loss of the kidney function over time. Symptoms usually develop slowly, and it may not appear in early stages. Lab tests can confirm a CKD diagnosis. The approximate number of incidents per year is more than 200,000 cases, and approximately 30 million people are living with CKD today in the United States. This long-standing disease ultimately leads to renal failure at the end. At this present time, there are no known cures for CKD, and the only treatment available is dialysis. Objectives The purpose of this study is to determine
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Ackerman, Hans Christian. "Disease association mapping : methods and markers." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249649.

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Burnett, Kimberley Anne. "The association of Alzheimer's disease and hypertension." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702224.

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Recent studies have suggested a link between Alzheimer's Disease (AD) and Hypertension (HTN) with midlife high blood pressure known to increase the risk of sporadic AD in later life, by approximately 1.5 fold. The molecular pathways and/or mechanisms that may link these two disease are yet to be fully understood, however evidence from a combination of observational human studies, animal models of HTN and the induction of HTN in animal models of AD implicated the renin angiotensin system, cerebral hypoperfusion, neuroinflammation, vascular dysfunction and oxidative stress in the association of
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Zanella, Ricardo. "Genetic association of tolerance to Johne's disease." Pullman, Wash. : Washington State University, 2008. http://www.dissertations.wsu.edu/Thesis/Fall2008/r_zanella_120308.pdf.

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Thesis (M.S. in animal sciences)--Washington State University, December 2008.<br>Title from PDF title page (viewed on Feb. 19, 2009). "Department of Animal Sciences." Includes bibliographical references.
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Andrieux, Sybil. "THE ASSOCIATION BETWEEN INSULIN AND LIVER DISEASE." Master's thesis, Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/593548.

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Epidemiology<br>M.S.<br>Introduction: Over the past three decades, the prevalence of diabetes has steadily increased. Additionally, diabetes has contributed to more diagnosed cases of liver disease (LD) suggesting that liver health is increasingly vulnerable to insulin levels. Several studies have found that endogenous insulin levels were important indicators in the development and progression of LD. However, to our knowledge, no studies have assessed the association of exogenous insulin intake and LD. Methods: Using nationally representative data from the 2013-2014 (n=5769) and 2015-2016 (n=5
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Jostins, Luke. "Using next-generation genomic datasets in disease association." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608054.

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Wei, Ruipeng. "Association study of mitochondrial genome and cardiovascular disease." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1554479244080773.

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Newcombe, Paul. "Bayesian evidence synthesis of gene-disease association studies." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536761.

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Alaei, Shahmiri Fariba. "The potential association between thiamin, hyperglycemia and chronic diseases." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1209.

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Chronic diseases, such as cardiovascular disease and diabetes, are the leading causes of morbidity and mortality worldwide. Recent studies have shown that in addition to diabetes mellitus, non-diabetic degrees of fasting and postprandial hyperglycemia are also directly linked to accelerated risks of cardiovascular diseases. Thiamin is a water soluble vitamin playing a key regulatory role as a co-enzyme in metabolic pathways implicated in the glucose metabolism. There is some evidence that diabetic patients are prone to thiamin deficiency possibly because of an increased excretion of thiamin in
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Katzov, Hagit. "Genetic association analysis of overlapping biological pathways in cardiovascular and Alzheimer disease /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-787-1/.

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Livros sobre o assunto "Disease association"

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Verfasser, Kenawy Sanaa A., and El Sisi, Rehab W. Verfasser, eds. Behçet's disease and subclinical atherosclerosis: An overlooked association. LAP LAMBERT Academic Publishing, 2013.

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Zeggini, Eleftheria. Analysis of complex disease association studies: A practical guide. Academic Press/Elsevier, 2011.

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American Association of Zoo Veterinarians. Conference. Proceedings: American Association of Zoo Veterinarians, Wildlife Disease Association, American Association of Wildlife Veterinarians, joint conference, August 12-17, 1995. Edited by Junge Randall E, American Association of Zoo Veterinarians., Wildlife Disease Association, American Association of Wildlife Veterinarians., and Wildlife Disease Association. International Conference. The Associations, 1995.

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Emmanuel, Hirsch. Aides, solidaires. Cerf, 1991.

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Association, American Bar. American Bar Association policy and report on AIDS. ABA AIDS Coordination Project, 1990.

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Association, American Medical, ed. American medical association complete guide to children's health. Random House, 1999.

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Grady, Alicia Mary. The molecular neuropathology of Alzheimer's disease and its association with Down's syndrome. University of Manchester, 1996.

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Association, British Medical, ed. The British Medical Association family doctor guide to cholesterol and heart disease. Family Doctore Publications., 1999.

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Association, Canadian Celiac, ed. The Canadian Celiac Association handbook: Celiac disease needs a diet for life. 3rd ed. Canadian Celiac Association = Association canadienne de la maladie coeliaque, 1993.

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Clark, Carolyn Chambers. American Holistic Nurses' Association Guide to Common Chronic Conditions. John Wiley & Sons, Ltd., 2002.

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Capítulos de livros sobre o assunto "Disease association"

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Chang, Ling-Sai. "Allergic Diseases and Association with Kawasaki Disease." In Kawasaki Disease. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2944-1_20.

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Balemans, Wendy, Wim Van Hul, Marian Valko, et al. "VATER Association." In Encyclopedia of Molecular Mechanisms of Disease. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-29676-8_1824.

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Balemans, Wendy, Wim Van Hul, Marian Valko, et al. "VACTERL Association." In Encyclopedia of Molecular Mechanisms of Disease. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-29676-8_7144.

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Peters, Nils, Martin Dichgans, Sankar Surendran, et al. "CHARGE Association." In Encyclopedia of Molecular Mechanisms of Disease. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-29676-8_7575.

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Garrido, Federico. "HLA and Disease Association." In The Major Histocompatibility Complex (MHC/ HLA) in Medicine. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59866-1_6.

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Ozaki, Kouichi, and Toshihiro Tanaka. "Genetics of Coronary Disease." In Genome-Wide Association Studies. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8177-5_2.

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LaxmiKanth, P., J. Akshitha, P. Akshitha, and D. Abhinandhan. "Classification for Disease Gene Association." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51338-1_18.

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Coleman, Ciara, Emma M. Quinn, and Ross McManus. "Quality Control Procedures for High-Throughput Genetic Association Studies." In Celiac Disease. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2839-2_17.

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Rhee, Peter Charles, and Steven L. Moran. "Lunate Bone Morphology and Its Association with Kienböck’s Disease." In Kienböck’s Disease. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34226-9_4.

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Khachaturian, Z. S., and T. S. Radebaugh. "The Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association." In Alzheimer Disease. Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-4116-4_91.

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Trabalhos de conferências sobre o assunto "Disease association"

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Pogány, Domonkos, and Péter Antal. "Hyperbolic Metabolite-Disease Association Prediction." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-29.

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Liu, Yanrong, and Lijun Ren. "Application of Association Rule Algorithm in Chronic Disease Prediction." In 2024 International Conference on Intelligent Computing and Robotics (ICICR). IEEE, 2024. http://dx.doi.org/10.1109/icicr61203.2024.00034.

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Wang, Jiaxuan, Jiajie Xing, and Juan Wang. "Predicting miRNA-disease association based on Hybrid Graph Autoencoder." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821965.

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Yin, Zhe, Zhenfei Yan, Teng Liu, and Daying Lu. "Predicting microRNA-disease association by autoencoder and extreme gradient boosting." In 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), edited by Pier Paolo Piccaluga, Ahmed El-Hashash, and Xiangqian Guo. SPIE, 2024. http://dx.doi.org/10.1117/12.3044089.

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Zhang, Hanwen. "Prediction of miRNA-mRNA-Disease Association Using Network Consistency Projection." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10913094.

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Zhou, Yi, Xian Guan, Meixuan Wu, Chengzhou Ouyang, and Min Zhu. "Timely-MDA: A Benchmark for Generalizable MiRNA-Disease Association Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822171.

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Zhang, Kexin, Feng Huang, Luotao Liu, et al. "Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/681.

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The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, m
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Liu, Luotao, Feng Huang, Xuan Liu, et al. "Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/537.

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Identifying the potential associations among drugs, microbes and diseases is of great significance in exploring the pathogenesis and improving precision medicine. There are plenty of computational methods for pair-wise association prediction, such as drug-microbe and microbe-disease associations, but few methods focus on the higher-order triple-wise drug-microbe-disease (DMD) associations. Driven by the advancement of hypergraph neural networks (HGNNs), we expect them to fully capture high-order interaction patterns behind the hypergraph formulated by DMD associations and realize sound predict
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Zhang, Wen, Xiang Yue, Yanlin Chen, et al. "Predicting drug-disease associations based on the known association bipartite network." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217698.

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Zhang, Guo-Zheng, Shu-Zhen Li, Xu-Ran Dou, Junliang Shang, Qian-Qian Ren, and Ying-Lian Gao. "Predicting LncRNA-Disease Associations Based on LncRNA-MiRNA-Disease Multilayer Association Network and Bipartite Network Recommendation." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995182.

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Relatórios de organizações sobre o assunto "Disease association"

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Wang, Yan, and Guowei Che. Association of gastroesophageal reflux disease with risk of pulmonary diseases. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.9.0013.

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Li, Mingqiang, Juan Wan, and Zhenhong Xu. The association between Parkinson’s disease and autoimmune diseases: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.8.0088.

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Zhao, Lei, Lan Wei, and Xiaolu Fei. Association between triglyceride glucose index and peripehral artery disease. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.7.0067.

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Du, Yuhan, Jiajun Li, Xinchao Huang, and shujing Wu. Association Between Serum Adiponectin And Non-alcoholic Fatty Liver Disease. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.5.0080.

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Zubizarreta-Macho, Álvaro. Association of periodontal disease with prostatitis. Systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.5.0030.

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Liu, Liushu, and Zhiyou Cai. Association between cerebral small diseases and the risk of Parkinson disease: a systematic review and meta analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.8.0083.

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Sheng, Chang, Weihua Huang, Mingmei Liao, and Pu Yang. Association of the Abdominal Aortic Calcification with All-Cause and Cardiovascular Disease-Specific Mortality: Prospective Cohort Study. World Journal of Surgery, 2024. http://dx.doi.org/10.60123/j.wjs.2024.10.03.

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Background: Abdominal aortic calcification (AAC) is a prevalent form of vascular calcification associated with adverse cardiovascular outcomes. While previous studies on AAC and cardiovascular risk exist, many have limitations such as small sample sizes and limited clinical significance outcomes. This study aims to prospectively investigate the association between AAC and all-cause and cardiovascular disease (CVD)-specific mortality rates in a nationally representative sample of adults in the United States, using data from the National Health and Nutrition Examination Survey (NHANES). Methods:
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Cesare Saltini, Massimo Amicosante. Analysis of HLA-DP association with beryllium disease susceptibility in pooled exposed populations. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/969219.

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Kapellas, Kostas. The association between periodontal disease and dementia: a systematic review and meta-analysis. Science Repository, 2019. http://dx.doi.org/10.31487/j.dobcr.2019.01.005.

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Wu, Jinyi, Junwen Wang, Qingsong Zhang, Jun Jiang, Qingwu Jiang, and Yibiao Zhou. Association between cardiovascular disease and hip fracture: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.11.0090.

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