scholarly journals Predicting metabolite-disease associations based on KATZ model

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiujuan Lei ◽  
Cheng Zhang
Keyword(s):  
Case Studies ◽  
Semantic Similarity ◽  
Medical Equipment ◽  
Predictive Performance ◽  
Computational Method ◽  
Pathological Changes ◽  
Disease Similarity ◽  
Disease Associations ◽  
Measuring Tool ◽  
First Time

Abstract Background Increasing numbers of evidences have illuminated that metabolites can respond to pathological changes. However, identifying the diseases-related metabolites is a magnificent challenge in the field of biology and medicine. Traditional medical equipment not only has the limitation of its accuracy but also is expensive and time-consuming. Therefore, it’s necessary to take advantage of computational methods for predicting potential associations between metabolites and diseases. Results In this study, we develop a computational method based on KATZ algorithm to predict metabolite-disease associations (KATZMDA). Firstly, we extract data about metabolite-disease pairs from the latest version of HMDB database for the materials of prediction. Then we take advantage of disease semantic similarity and the improved disease Gaussian Interaction Profile (GIP) kernel similarity to obtain more reliable disease similarity and enhance the predictive performance of our proposed computational method. Simultaneously, KATZ algorithm is applied in the domains of metabolomics for the first time. Conclusions According to three kinds of cross validations and case studies of three common diseases, KATZMDA is worth serving as an impactful measuring tool for predicting the potential associations between metabolites and diseases.

scholarly journals NEDD: a network embedding based method for predicting drug-disease associations

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Renyi Zhou ◽  
Zhangli Lu ◽  
Huimin Luo ◽  
Ju Xiang ◽  
Min Zeng ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Computational Method ◽  
Heterogeneous Information ◽  
Gold Standard Dataset ◽  
Disease Similarity ◽  
Disease Associations ◽  
Meta Path ◽  
Approved Drugs ◽  
Low Dimensional ◽  
Novel Associations

Abstract Background Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. Results In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. Conclusions The experiments on a gold standard dataset which contains 1933 validated drug–disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

scholarly journals PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations

2018 ◽  
Vol 19 (11) ◽  
pp. 3410 ◽  
Author(s):  
Xiujuan Lei ◽  
Zengqiang Fang ◽  
Luonan Chen ◽  
Fang-Xiang Wu
Keyword(s):  
Semantic Similarity ◽  
Heterogeneous Network ◽  
Functional Similarity ◽  
Computational Method ◽  
Related Gene ◽  
Similarity Network ◽  
Disease Pair ◽  
Disease Associations ◽  
Leave One Out ◽  
Similarity Scores

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 gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

scholarly journals gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Li Wang ◽  
Cheng Zhong
Keyword(s):  
Characteristic Curve ◽  
Experimental Results ◽  
Computational Method ◽  
Attention Network ◽  
Feature Vectors ◽  
Disease Similarity ◽  
Potential Association ◽  
Receiver Operation Characteristic ◽  
Disease Pair ◽  
Disease Associations

Abstract Background Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance. Results In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer. Conclusion The experimental results indicate that our method is a useful approach for predicting potential LDAs.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

2019 ◽  
Vol 21 (4) ◽  
pp. 1356-1367 ◽  
Author(s):  
Hang Wei ◽  
Bin Liu
Keyword(s):  
Matrix Factorization ◽  
Complex Disease ◽  
False Negative ◽  
Computational Method ◽  
Circular Rnas ◽  
Disease Mechanism ◽  
Disease Similarity ◽  
The Matrix ◽  
Disease Associations ◽  
Non Coding Rnas

Abstract Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

scholarly journals The SN2 reaction and its relationship with the Walden inversion, the Finkelstein and Menshutkin reactions together with theoretical calculations for the Finkelstein reaction

2021 ◽  
Author(s):  
Ibon Alkorta ◽  
José Elguero
Keyword(s):  
Linear Models ◽  
Theoretical Calculations ◽  
Computational Method ◽  
Basis Set ◽  
Free Energies ◽  
Nitrogen And Phosphorus ◽  
Atom Pairs ◽  
Leaving Groups ◽  
First Time ◽  
Nitrogen Phosphorus

AbstractThis communication gives an overview of the relationships between four reactions that although related were not always perceived as such: SN2, Walden, Finkelstein, and Menshutkin. Binary interactions (SN2 & Walden, SN2 & Menshutkin, SN2 & Finkelstein, Walden & Menshutkin, Walden & Finkelstein, Menshutkin & Finkelstein) were reported. Carbon, silicon, nitrogen, and phosphorus as central atoms and fluorides, chlorides, bromides, and iodides as lateral atoms were considered. Theoretical calculations provide Gibbs free energies that were analyzed with linear models to obtain the halide contributions. The M06-2x DFT computational method and the 6-311++G(d,p) basis set have been used for all atoms except for iodine where the effective core potential def2-TZVP basis set was used. Concerning the central atom pairs, carbon/silicon vs. nitrogen/phosphorus, we reported here for the first time that the effect of valence expansion was known for Si but not for P. Concerning the lateral halogen atoms, some empirical models including the interaction between F and I as entering and leaving groups explain the Gibbs free energies.

La Guerre en dentelles: Women and the Prix de Rome in French Cultural Politics

1998 ◽  
Vol 51 (1) ◽  
pp. 83-129 ◽  
Author(s):  
Annegret Fauser
Keyword(s):  
Twentieth Century ◽  
Case Studies ◽  
Cultural Politics ◽  
Music Composition ◽  
Public Recognition ◽  
Nadia Boulanger ◽  
First Time

In 1903, one hundred years after the Prix de Rome had been created in music composition, women were allowed to participate in the competition for the first time. In 1913, Lili Boulanger became the first woman to win the prize, crowning the efforts of three others-Juliette Toutain, Hélène Fleury, and Nadia Boulanger-to achieve this goal. Their stories are fascinating case studies of the strategies women employed to achieve success and public recognition within the complex framework of French cultural politics at the beginning of the twentieth century.

Clinico-Pathological Correlations of the Frontal Lobe Syndrome: Results of a Large Brain Bank Study

2015 ◽  
Vol 40 (3-4) ◽  
pp. 121-129
Author(s):  
Welmoed A. Krudop ◽  
Sjanne Bosman ◽  
Jeroen J.G. Geurts ◽  
Sietske A.M. Sikkes ◽  
Nicolaas A. Verwey ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Grey Matter ◽  
Pathological Changes ◽  
Brain Bank ◽  
Large Brain ◽  
Cortical Pathology ◽  
Brain Changes ◽  
Subcortical Regions ◽  
Frontal Lobe Syndrome ◽  
First Time

Aims: A clinical frontal lobe syndrome (FLS) is generally attributed to functional or structural disturbances within frontal-subcortical circuits. We studied the distribution of pathological brain changes in FLS. Additionally, the prevalence of FLS among various disorders was studied. Methods: We systematically screened clinical files of donors to the Netherlands Brain Bank (n = 2,814) for FLS. A total of 262 FLS cases were identified, and the distribution of postmortem pathological changes within the frontal-subcortical circuits was extracted from their neuropathological reports. Results: In 244 out of 262 patients (93%), pathological changes within the frontal-subcortical circuits were found: 90 subjects (34%) with frontal cortical pathology and 18 (7%) with pathology restricted to subcortical grey matter nuclei, whereas 136 subjects (52%) showed both cortical and subcortical pathology. In 18 subjects (7%), no pathology was found in the examined areas. The prevalence of FLS was highest in frontal-temporal lobar degeneration, followed by progressive supranuclear palsy and vascular dementia [χ2(6, n = 1,561) = 222.64, p < 0.01]. Conclusion: In this large brain bank study, the distribution of pathological changes in subjects with FLS was shown to be frontal-subcortical for the first time. A minority of FLS cases had pathology in the subcortical regions only or no frontal pathology at all.

“Never the first time on a patient”: the stakes of high-fidelity simulation for safety training

2018 ◽  
Vol 32 (5) ◽  
pp. 23-25 ◽  
Author(s):  
Lucie Cuvelier
Keyword(s):  
Case Studies ◽  
Design Methodology ◽  
Reading Time ◽  
Safety Training ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Content Type ◽  
Pertinent Information ◽  
First Time ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings An operative approach is described that is designed to structure the debriefing along three axes. Practical implications The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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