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Journal articles on the topic 'Disease association'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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Xuan, Ye, Zhang, Zhao, and Sun. "Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug–Disease Associations." Cells 8, no. 7 (2019): 705. http://dx.doi.org/10.3390/cells8070705.

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Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)—CBPred—for predicting drug-related di

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Svejgaard, Arne, and Lars P. Ryder. "HLA and disease associations: Detecting the strongest association." Tissue Antigens 43, no. 1 (1994): 18–27. http://dx.doi.org/10.1111/j.1399-0039.1994.tb02291.x.

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Huang, Wenhui, Pengyuan Wang, Zhen Liu, and Liqing Zhang. "Identifying disease associations via genome-wide association studies." BMC Bioinformatics 10, Suppl 1 (2009): S68. http://dx.doi.org/10.1186/1471-2105-10-s1-s68.

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Xing, Zhengzheng, and Jian Pei. "Exploring Disease Association from the NHANES Data." International Journal of Data Warehousing and Mining 6, no. 3 (2010): 11–27. http://dx.doi.org/10.4018/jdwm.2010070102.

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Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques to test a small number of hypotheses. This NHANES data has not been systematically explored for mining disease association patterns. In this regard, this paper proposes a direct disease pattern mining method and an interactive disease pattern mining method to explore the NHANES data. The results on the latest NHANES data demonstrate that these m

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Hou, Jialu, Hang Wei, and Bin Liu. "iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network." PLOS Computational Biology 19, no. 6 (2023): e1011242. http://dx.doi.org/10.1371/journal.pcbi.1011242.

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Accurately identifying potential piRNA-disease associations is of great importance in uncovering the pathogenesis of diseases. Recently, several machine-learning-based methods have been proposed for piRNA-disease association detection. However, they are suffering from the high sparsity of piRNA-disease association network and the Boolean representation of piRNA-disease associations ignoring the confidence coefficients. In this study, we propose a supplementarily weighted strategy to solve these disadvantages. Combined with Graph Convolutional Networks (GCNs), a novel predictor called iPiDA-SWG

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Browning, Michael. "Disease by association." Trends in Immunology 22, no. 5 (2001): 283. http://dx.doi.org/10.1016/s1471-4906(01)01899-3.

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Abdelbadeea, E. E., M. A. El-Dosuky, and M. Z. Rashad. "Gene-Disease Association." Mansoura Journal for Computer and Information Sciences 16, no. 2 (2020): 1–9. http://dx.doi.org/10.21608/mjcis.2020.321071.

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Chen, Xing, Zhi-Chao Jiang, Di Xie, et al. "A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction." Molecular BioSystems 13, no. 6 (2017): 1202–12. http://dx.doi.org/10.1039/c6mb00853d.

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Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction (SDMMDA) to predict potential miRNA–disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs.

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Herrera, David, Ana Molina, Kare Buhlin, and Bjorn Klinge. "Periodontal diseases and association with atherosclerotic disease." Periodontology 2000 83, no. 1 (2020): 66–89. http://dx.doi.org/10.1111/prd.12302.

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Kim, Yoonbee, and Young-Rae Cho. "Predicting Drug–Gene–Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning." Biomedicines 11, no. 7 (2023): 1998. http://dx.doi.org/10.3390/biomedicines11071998.

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Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug–gene–disease tensor by integrating drug–disease, drug–gene, and disease–gene associations and predict drug–gene–disease tripl

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Allahdadi, Atefe. "A Rare Association of Three Concurrent Autoimmune Diseases in One Patient." Gastroenterology & Hepatology International Journal 4, no. 2 (2019): 1–3. http://dx.doi.org/10.23880/ghij-16000158.

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Introduction: Celiac disease is a treatable gluten-induced disease that often occurs concurrently with other autoimmune diseases. It is commonly associated with a number of extra gastrointestinal manifestations which makes it a systematic disease. Here we report a case of in which three concurrent autoimmune disease including celiac disease, autoimmune hepatitis, and inflammatory bowel disease are present. Case report: A 9 years old girl was evaluated for growth retardation and unexplained elevation of aminotransferase enzymes. Serological tests revealed that she suffers from celiac disease, l

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Wang, Li, and Cheng Zhong. "Prediction of miRNA-Disease Association Using Deep Collaborative Filtering." BioMed Research International 2021 (February 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/6652948.

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The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential as

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Bang, Dongmin, Jeonghyeon Gu, Joonhyeong Park, et al. "A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective." International Journal of Molecular Sciences 23, no. 19 (2022): 11498. http://dx.doi.org/10.3390/ijms231911498.

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Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related bi

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Jiang, Lei, and Ji Zhu. "Review of MiRNA-Disease Association Prediction." Current Protein & Peptide Science 21, no. 11 (2020): 1044–53. http://dx.doi.org/10.2174/1389203721666200210102751.

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: Accumulating evidence demonstrates that miRNAs serve as critical biomarkers in various complex human diseases. Thus, identifying potential miRNA-disease associations has become a hot research topic for providing a better understanding of disease pathology, including cell carcinoma, cell proliferation and mevalonate pathway. Recently, based on various biological datasets, more and more computational prediction methods have been designed to uncover disease-related miRNAs for further experimental validation. Due to the fact that different limitations exist in previous computational methods, we

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MATSUNAGA, TSUTOMU, SHUHEI KUWATA, and MASAAKI MURAMATSU. "COMPUTATIONAL GENE KNOCKOUT REVEALS TRANSDISEASE–TRANSGENE ASSOCIATION STRUCTURE." Journal of Bioinformatics and Computational Biology 08, no. 05 (2010): 843–66. http://dx.doi.org/10.1142/s0219720010004975.

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Genome-wide association studies for a variety of diseases are identifying increasing numbers of candidate genes. Now we are confronted with the fact that some genes are common candidates across diseases. Thus there is a strong need to develop a hypothesis formulation methodology to comprehend multifaceted associations between genes and diseases. We have developed a computational method for building transdisease–transgene association structure. By introducing the basic rationale underlying the gene knockout approach as an information processing procedure to a network constructed on the basis of

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Li, Xiaoying, Yaping Lin, and Changlong Gu. "A network similarity integration method for predicting microRNA-disease associations." RSC Advances 7, no. 51 (2017): 32216–24. http://dx.doi.org/10.1039/c7ra05348g.

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The NSIM integrates the disease similarity network, miRNA similarity network, and known miRNA-disease association network on the basis of cousin similarity to predict not only novel miRNA-disease associations but also isolated diseases.

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Xuan, Zhanwei, Jiechen Li, Jingwen Yu, Xiang Feng, Bihai Zhao, and Lei Wang. "A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations." Genes 10, no. 2 (2019): 126. http://dx.doi.org/10.3390/genes10020126.

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Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we

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S. A Hosseni, S. A. Hosseni. "Association of Cancer Disease and 226Ra Radiation Exposure in Water." Indian Journal of Applied Research 4, no. 2 (2011): 24–2. http://dx.doi.org/10.15373/2249555x/feb2014/112.

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Mustafina, A. F., M. K. Mytnik, S. I. Kramskikh, et al. "Association of celiac disease and Crohn’s disease." Voprosy detskoj dietologii 21, no. 5 (2023): 71–80. http://dx.doi.org/10.20953/1727-5784-2023-5-71-80.

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The incidence of immune-mediated diseases in children is increasing worldwide. The prevalence of Crohn’s disease reaches 11.4 cases per 100,000 people. The estimated incidence of celiac disease in Russia is 1:100–1:250. The association of Crohn’s disease and celiac disease is quite common. Celiac disease is associated with an 11-fold increase in the risk of inflammatory bowel disease (IBD) compared to control populations; patients with IBD have a 2-fold increased risk for developing celiac disease. This may be due to similarities in pathogenesis. Identical ultrastructural changes in the intest

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Dilliott, Allison A., Emily C. Evans, Sali M. K. Farhan, et al. "Genetic Variation in the Ontario Neurodegenerative Disease Research Initiative." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 46, no. 5 (2019): 491–98. http://dx.doi.org/10.1017/cjn.2019.228.

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ABSTRACT:Background/Objective:Apolipoprotein E (APOE) E4 is the main genetic risk factor for Alzheimer’s disease (AD). Due to the consistent association, there is interest as to whether E4 influences the risk of other neurodegenerative diseases. Further, there is a constant search for other genetic biomarkers contributing to these phenotypes, such as microtubule-associated protein tau (MAPT) haplotypes. Here, participants from the Ontario Neurodegenerative Disease Research Initiative were genotyped to investigate whether the APOE E4 allele or MAPT H1 haplotype are associated with five neurodeg

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Buchke, Sakshi, Anusuiya Bora, Bhavika Mehta, et al. "Celiac Disease and Its Association with Organ-Specific Auto-Immune Diseases." International Journal of Innovative Research in Medical Science 6, no. 10 (2021): 687–97. http://dx.doi.org/10.23958/ijirms/vol06-i10/1214.

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Wheat is one of the most consumed foods in the world. Although it is extremely nutrient rich for us humans, some of us have great difficulties in completely digesting its protein subunits. This review aims to understand the onset of Celiac Disease and its association with several other auto-immune diseases. The gliadin molecule, undigested in the small intestine, over time, ruptures the villi lining of the intestinal wall and enters the bloodstream which in turn activates the body's immune response. In some patients with the presence of HLA DQ2/DQ8 genes, this immune response results in Celiac

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Fozia, Jamal, Saxena Shikha, Srivastava P.C., and Seth Seema. "Paraoxonase-1: Genetic Variations and Disease Association." International Journal of Pharmaceutical and Clinical Research 15, no. 9 (2023): 817–22. https://doi.org/10.5281/zenodo.11357697.

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The association between Paraoxonase 1 (PON-1) polymorphisms and diseases has been a subject of extensive research. PON-1 is an enzyme involved in protecting against oxidative stress and inflammation. Numerous studies have investigated the potential links between PON-1 polymorphisms and various diseases, including cardiovascular diseases, diabetes, neurological disorders, liver diseases, and cancer. However, the findings from these studies have been inconsistent and often conflicting. While some studies have reported significant associations between specific PON-1 polymorphisms and increased or

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Zhang, Wenjuan, Hunan Xu, Xiaozhong Li, Qiang Gao, and Lin Wang. "DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion." Bioinformatics 36, no. 9 (2020): 2839–47. http://dx.doi.org/10.1093/bioinformatics/btaa062.

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Abstract Motivation One of the most important problems in drug discovery research is to precisely predict a new indication for an existing drug, i.e. drug repositioning. Recent recommendation system-based methods have tackled this problem using matrix completion models. The models identify latent factors contributing to known drug-disease associations, and then infer novel drug-disease associations by the correlations between latent factors. However, these models have not fully considered the various drug data sources and the sparsity of the drug-disease association matrix. In addition, using

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Liu, Yang, Xiang Feng, Haochen Zhao, Zhanwei Xuan, and Lei Wang. "A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association." International Journal of Molecular Sciences 20, no. 7 (2019): 1549. http://dx.doi.org/10.3390/ijms20071549.

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Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease

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Zou, Longchun, and Chunlei Zhang. "Association of Periodontal Disease with Systemic Diseases – Update." International Journal of Applied Science and Research 06, no. 03 (2023): 14–18. http://dx.doi.org/10.56293/ijasr.2022.5520.

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Periodontitis is a state of inflammation caused by bacteria in the oral cavity. Typical clinical symptoms of periodontitis include gum inflammation, alveolar bone loss, clinical attachment loss and periodontal pocket formation.Through the years, the association of periodontal disease with other non-infectious systemic diseases has been brought to attention.A growing body of scientific evidence has shown that severe periodontitis may enhance susceptibility to certain important systemic diseases and conditions, for example, cardiovascular disease, diabetes mellitus, adverse pregnancy outcomes, c

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Lee, Jae-Hong, Jin-Young Oh, Tae-Mi Youk, Seong-Nyum Jeong, Young-Taek Kim, and Seong-Ho Choi. "Association between periodontal disease and non-communicable diseases." Medicine 96, no. 26 (2017): e7398. http://dx.doi.org/10.1097/md.0000000000007398.

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Merghit, Rachid, Ikhlas Gueriane, Mouloud Ait Athmane, and Abdelhak Lakehal. "Prise en charge de la maladie poly artérielle : résultats d’une étude transversale monocentrique à l’est Algérien." Batna Journal of Medical Sciences (BJMS) 8, no. 1 (2021): 19–23. http://dx.doi.org/10.48087/bjmsoa.2021.8104.

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Introduction. The rates of atherosclerotic disease as well as its multifocal aspects have been increasing significantly. It is important to know these associations to ensure comprehensive management of this category of patients. Aim. To estimate the frequency of the principal peripheral atherosclerotic associations in patients with coronary artery disease referred to cardiology in the University Hospital of Constantine. Methods. Our study is descriptive, cross-sectional, and mono-centric carried out in the unit of cardiovascular investigations of the Regional Military University Hospital of Co

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Ramly, Balqis, Nor Afiqah-Aleng, and Zeti-Azura Mohamed-Hussein. "Protein–Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome." International Journal of Molecular Sciences 20, no. 12 (2019): 2959. http://dx.doi.org/10.3390/ijms20122959.

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Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related pro

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Lei, Xiujuan, Zengqiang Fang, Luonan Chen, and Fang-Xiang Wu. "PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations." International Journal of Molecular Sciences 19, no. 11 (2018): 3410. http://dx.doi.org/10.3390/ijms19113410.

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CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gen

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Kuksa, Pavel P., Chia-Lun Liu, Wei Fu, et al. "Alzheimer’s Disease Variant Portal: A Catalog of Genetic Findings for Alzheimer’s Disease." Journal of Alzheimer's Disease 86, no. 1 (2022): 461–77. http://dx.doi.org/10.3233/jad-215055.

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Background: Recent Alzheimer’s disease (AD) genetics findings from genome-wide association studies (GWAS) span progressively larger and more diverse populations and outcomes. Currently, there is no up-to-date resource providing harmonized and searchable information on all AD genetic associations found by GWAS, nor linking the reported genetic variants and genes with functional and genomic annotations. Objective: Create an integrated/harmonized, and literature-derived collection of population-specific AD genetic associations. Methods: We developed the Alzheimer’s Disease Variant Portal (ADVP),

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Li, Jin, Sai Zhang, Tao Liu, Chenxi Ning, Zhuoxuan Zhang, and Wei Zhou. "Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction." Bioinformatics 36, no. 8 (2020): 2538–46. http://dx.doi.org/10.1093/bioinformatics/btz965.

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Abstract Motivation Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA. Results We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks t

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Trabace, Simonetta. "HLA and disease association." Journal of Headache and Pain 1, S2 (2000): S109—S113. http://dx.doi.org/10.1007/s101940070003.

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Hamed, Abd Elkhalek, Nadia Elwan, Mervat Naguib, et al. "Diabetes Association with Liver Diseases: An Overview for Clinicians." Endocrine, Metabolic & Immune Disorders - Drug Targets 19, no. 3 (2019): 274–80. http://dx.doi.org/10.2174/1871530318666181116111945.

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Background: There is a strong association between liver diseases and diabetes (DM) which is higher than expected by a correlation between two very common diseases. Liver diseases may occur as a result of diabetes, and the reverse is true as well. Aim: To review the etiology of this association between liver diseases and diabetes and how to diagnose it. Methods: Studies that identified this association between liver diseases and diabetes and how to diagnose it was reviewed. Results: his association can be divided into the following categories: liver disease related to diabetes (Diabetic hepatop

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Tang, Lin, Yu Liang, Xin Jin, Lin Liu, and Wei Zhou. "Hierarchical Extension Based on the Boolean Matrix for LncRNA-Disease Association Prediction." Current Molecular Medicine 20, no. 6 (2020): 452–60. http://dx.doi.org/10.2174/1566524019666191119104212.

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Background: Accumulating experimental studies demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA–disease associations have been experimentally verified at present. Automatically predicting LncRNA–disease associations based on computational models can save the huge cost of wet-lab experiments. Methods and Result: To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we

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Kartheeswaran, Karpaga Priyaa, Arockia Xavier Annie Rayan, and Geetha Thekkumpurath Varrieth. "Enhanced disease-disease association with information enriched disease representation." Mathematical Biosciences and Engineering 20, no. 5 (2023): 8892–932. http://dx.doi.org/10.3934/mbe.2023391.

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<abstract> <sec><title>Objective</title><p>Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. Materials and</p> </sec> <sec><title>Methods</title><p>An enhanced and integrated DDA framework is developed that integrates enriche

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N/A. "Periodontal Disease and Respiratory Disease: An Association." Biological Therapies in Dentistry 21, no. 05 (2006): 19. http://dx.doi.org/10.2310/7040.2005.21314.

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Carter, Paul, Jakub Lagan, Christien Fortune, et al. "Association of Cardiovascular Disease With Respiratory Disease." Journal of the American College of Cardiology 73, no. 17 (2019): 2166–77. http://dx.doi.org/10.1016/j.jacc.2018.11.063.

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Sun, Wei, Chang Guo, Jing Wan, and Han Ren. "piRNA-disease association prediction based on multi-channel graph variational autoencoder." PeerJ Computer Science 10 (July 23, 2024): e2216. http://dx.doi.org/10.7717/peerj-cs.2216.

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Piwi-interacting RNA (piRNA) is a type of non-coding small RNA that is highly expressed in mammalian testis. PiRNA has been implicated in various human diseases, but the experimental validation of piRNA-disease associations is costly and time-consuming. In this article, a novel computational method for predicting piRNA-disease associations using a multi-channel graph variational autoencoder (MC-GVAE) is proposed. This method integrates four types of similarity networks for piRNAs and diseases, which are derived from piRNA sequences, disease semantics, piRNA Gaussian Interaction Profile (GIP) k

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Huang, Zhou, Yu Han, Leibo Liu, Qinghua Cui, and Yuan Zhou. "LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations." International Journal of Molecular Sciences 22, no. 24 (2021): 13607. http://dx.doi.org/10.3390/ijms222413607.

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MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA-disease associations, but few models are available to further prioritize causal miRNA-disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA-disease Causal

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Peswani, Amit R., Vernon J. Sequeira, Mizelle D’Silva, Shrikant Ghanwat, Palak P. Shah, and Anil C. Pinto. "Association between Gallstone Disease and Metabolic Syndrome." International Journal of Contemporary Medical Research [IJCMR] 6, no. 10 (2019). http://dx.doi.org/10.21276/ijcmr.2019.6.10.13.

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