scholarly journals 894Molecular epidemiology: building causal evidence within highly dimensioned studies

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Anne-Louise Ponsonby
Keyword(s):  
Systematic Approach ◽  
Causal Structure ◽  
Epidemiological Studies ◽  
Disease Association ◽  
High Dimensional ◽  
High Likelihood ◽  
Causal Explanations ◽  
Disease Causation ◽  
Disease Associations ◽  
Process Factors

Abstract Focus of Presentation This presentation provides a practical perspective on molecular epidemiologic analysis within a single high dimensional study to consider a causal question. Findings The work steps include special issues. Those include composite exposures and understanding of collinearity across predictors. Laboratory artefact needs to be minimised. Non-causal confounding is also important. This can occur when a factor is a determinant of outcome and the underlying association between exposure and the factor is non-causal. That is, the association arises due to chance, confounding or other bias rather than reflecting that exposure and the factor are causally related. The influence of laboratory processing features must be accounted for. This can be achieved by pre-processing measures to remove artefact or controlling for process factors. The work plan should allow the evaluation of alternative non-causal explanations for observed exposure-disease associations and strategies to obtain the highest level of causal inference possible within the study. Conclusions/Implications A systematic approach is required to work through a question a question set and obtain insights on not only the exposure-disease association but the multifactorial causal structure of the underlying data where possible. The appropriate inclusion of molecular findings will enhance the quest to better understand multifactorial disease causation in modern observational epidemiological studies. Key messages A systematic set of work steps is outlined to assess whether detected exposure-disease associations have a low and high likelihood of being attributed to non-causal and causal explanations respectively.

EHAI: Enhanced Human Microbe-Disease Association Identification

2020 ◽  
Vol 21 (11) ◽  
pp. 1078-1084
Author(s):  
Ruizhi Fan ◽  
Chenhua Dong ◽  
Hu Song ◽  
Yixin Xu ◽  
Linsen Shi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Human Health ◽  
Complex Diseases ◽  
Disease Association ◽  
Computational Results ◽  
Computational Approaches ◽  
Disease Diagnostics ◽  
Disease Associations ◽  
Association Discovery ◽  
Biological Tool

: 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 discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

scholarly journals Multiple Linear Regression Analysis of lncRNA–Disease Association Prediction Based on Clinical Prognosis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Wang ◽  
Jing Zhang
Keyword(s):  
Prostate Cancer ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Cancer Survival ◽  
Disease Association ◽  
The Body ◽  
Survival Prediction ◽  
Clinical Prognosis ◽  
Disease Associations ◽  
Auc Value

Long noncoding RNAs (lncRNAs) have an important role in various life processes of the body, especially cancer. The analysis of disease prognosis is ignored in current prediction on lncRNA–disease associations. In this study, a multiple linear regression model was constructed for lncRNA–disease association prediction based on clinical prognosis data (MlrLDAcp), which integrated the cancer data of clinical prognosis and the expression quantity of lncRNA transcript. MlrLDAcp could realize not only cancer survival prediction but also lncRNA–disease association prediction. Ultimately, 60 lncRNAs most closely related to prostate cancer survival were selected from 481 alternative lncRNAs. Then, the multiple linear regression relationship between the prognosis survival of 176 patients with prostate cancer and 60 lncRNAs was also given. Compared with previous studies, MlrLDAcp had a predominant survival predictive ability and could effectively predict lncRNA–disease associations. MlrLDAcp had an area under the curve (AUC) value of 0.875 for survival prediction and an AUC value of 0.872 for lncRNA–disease association prediction. It could be an effective biological method for biomedical research.

scholarly journals Characteristics of energy intake under-reporting in French adults

2013 ◽  
Vol 111 (7) ◽  
pp. 1292-1302 ◽  
Author(s):  
Isabelle Berta Vanrullen ◽  
Jean-Luc Volatier ◽  
Aurélie Bertaut ◽  
Ariane Dufour ◽  
Jean Dallongeville
Keyword(s):  
Sedentary Behaviour ◽  
Energy Intake ◽  
Dietary Habits ◽  
Economic Status ◽  
Dairy Product ◽  
Weight Perception ◽  
Epidemiological Studies ◽  
Disease Associations ◽  
Cereal Product ◽  
Nationally Representative

Energy intake under-reporting (UR) is a concern in nutritional epidemiological studies, as it may distort the relationships between dietary habits and health. Although UR is known to be associated with certain characteristics, few studies have investigated them in France. Therefore, the goal of the present study was to assess the prevalence and characteristics of UR in French adults. UR was defined according to Goldberg's classification. A sample of 1567 adults was drawn from the nationally representative French dietary survey (Individuelle Nationale des Consommations Alimentaires 2 2006–7). Food intake (7 d record), dietary habits, socio-economic status, region of residence, sedentary behaviour and weight perception variables were assessed. Multivariate logistic regression was used to investigate the associations between UR and a number of covariates. The overall prevalence of UR was 22·5 %, similar in men and women. In both sexes, UR was positively associated with overweight and protein intake and inversely associated with age. In women, UR was associated with eating lunch in the office, poor perception of diet quality and sedentary behaviour and was inversely associated with educational level, residence in the Paris region, cereal product intake and eating lunch in a friend's or family member's home. In men, UR was positively associated with a history of slimming and inversely associated with dairy product intake and eating lunch at a staff canteen. In conclusion, UR is prevalent in French adults and is associated with several different characteristics. It is important to take account of UR when investigating diet–disease associations in adults.

scholarly journals Nurses’ Perspectives About Reasons for Not Reporting Medical Errors in Educational Hospitals, Ahvaz, Iran

2021 ◽  
Author(s):  
Zahra Rahsepar ◽  
Farzad Faraji-Khiavi ◽  
Mansour Zahiri ◽  
Mohammadhosein Haghighizadeh
Keyword(s):  
Medical Errors ◽  
Health Care Quality ◽  
Analytical Study ◽  
Systematic Approach ◽  
Care Quality ◽  
Error Reporting ◽  
Measurement Tool ◽  
Work Experiences ◽  
Process Factors ◽  
Different Levels

Background: Reporting of medical errors is an approach to identify and prevent errors in hospitals. Objectives: The purpose of this study was to determine the barriers to error rError Reporting; Nurse; Hospital; Ahvazeport from the nurses’ viewpoints in Ahvaz Educational hospitals. Methods: This descriptive-analytical study was done on 206 nurses working in educational hospitals of Ahvaz selected by stratified random sampling. The measurement tool used in this study was a researcher-made questionnaire, which its validity was confirmed by content validity, and its reliability using Cronbach’s alpha was calculated to be 0.84. Data collection was performed from April to June 2019. Results: The causes of failure to error reporting included educational, attitudinal, process, structural, and managerial factors. The total mean score of the factors causing non-reporting of errors was 3.88 ± 0.53, which was between 3 and 4 (“important”). Also, educational, attitudinal, and process factors were reported as “very important” for nurses. Structural and managerial factors were rated reported “important” by nurses over 90% of nurses rated educational, attitudinal, and process factors as important and very important, and more than 70% of them rated structural and managerial factors as important and very important. Nurses with different levels of education or work experiences had different scores in reasons for not reporting errors. Conclusions: Some educational, attitudinal, process, structural, and managerial factors were critical reasons for not reporting errors. In order to reduce same errors in the future and promoting health care quality, officials need to develop strategies to remove barriers and consider the reasons for not reporting errors in nurses’ educational programs using team-based and forward-looking approaches, adopting an impersonal and systematic approach, and finally, modifying error reporting rules.

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

Exploring Disease Association from the NHANES Data

2011 ◽  
pp. 157-173
Author(s):  
Zhengzheng Xing ◽  
Jian Pei
Keyword(s):  
Pattern Mining ◽  
Disease Association ◽  
Medical Data ◽  
Mining Method ◽  
Medical Data Mining ◽  
Data Set ◽  
Hierarchical Tree ◽  
Disease Associations ◽  
Disease Pattern ◽  
Influence Graph

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 methods can mine meaningful disease associations consistent with the existing knowledge and literatures. Furthermore, this study provides summarization of the data set via a disease influence graph and a disease hierarchical tree.

scholarly journals DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 608 ◽  
Author(s):  
Yan Li ◽  
Junyi Li ◽  
Naizheng Bian
Keyword(s):  
Matrix Factorization ◽  
Disease Diagnosis ◽  
Disease Association ◽  
Bayesian Optimization ◽  
Model Parameters ◽  
Target Interaction ◽  
Disease Associations ◽  
Auc Value ◽  
Similarity Networks ◽  
Fold Cross Validation

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA–disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.

Computational drug repositioning based on multi-similarities bilinear matrix factorization

2020 ◽  
Author(s):  
Mengyun Yang ◽  
Gaoyan Wu ◽  
Qichang Zhao ◽  
Yaohang Li ◽  
Jianxin Wang
Keyword(s):  
Matrix Factorization ◽  
Drug Repositioning ◽  
Disease Association ◽  
Biological Entity ◽  
Biomedical Data ◽  
Supplementary Data ◽  
Practical Applications ◽  
Disease Associations ◽  
Association Matrix ◽  
Similarity Matrices

Abstract With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, drug–drug similarities can be measured from target profiles, drug–drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug–disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug–disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug–disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: [email protected] Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.

scholarly journals WBNPMD: weighted bipartite network projection for microRNA-disease association prediction

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Guobo Xie ◽  
Zhiliang Fan ◽  
Yuping Sun ◽  
Cuiming Wu ◽  
Lei Ma
Keyword(s):  
Cross Validation ◽  
Lung Neoplasm ◽  
Disease Association ◽  
Computational Techniques ◽  
Bipartite Network ◽  
Initial Information ◽  
Method Transfer ◽  
Disease Associations ◽  
Association Data ◽  
Leave One Out

Abstract Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and $$0.9173 \pm 0.0005$$ 0.9173 ± 0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation.

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