BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

Wu, Guanying; Guo, Xuan; Xu, Baohua

doi:10.26599/tst.2019.9010064

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…The area under the ROC curve is AUC. Moreover, a series of broader assessment criteria [25,26] , including the area under the precise-recall curve (AUPR), F1-measure (F1), accuracy (ACC), specificity (SPE), recall (REC), and precision (PRE), are used for more comprehensive and equitable evaluation of proposed models.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Metabolite-disease association prediction algorithm combining DeepWalk and random forest

Tie

Lei

Pan

2022

Tsinghua Sci. Technol.

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Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases, which has great significance in diagnosing and treating diseases. However, traditional biometric methods are time consuming and expensive. Accordingly, we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest (DWRF), which consists of the following key steps:First, the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity. Similarly, molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity. Then, DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations. Finally, a random forest algorithm is employed to infer metabolite-disease associations.The experimental results show that DWRF has good performances in terms of the area under the curve value, leave-one-out cross-validation, and five-fold cross-validation. Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.

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Section: Evaluation Metricsmentioning

confidence: 99%

Metabolite-disease association prediction algorithm combining DeepWalk and random forest

Tie

Lei

Pan

2022

Tsinghua Sci. Technol.

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“…For example, a powerful forward often means the decrement of whole team's defense ability as the forward is often not willing to take part in the defense job. In view of this exampled observation, an association rulebased similarity calculation and matching approach is brought forth in [31][32].…”

Section: A Similarity-based Matchingmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

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“…However, the above comprehensive score calculation solutions are often confronted with a big challenge, i.e., they often assume that the involved multiple sport training scores are independent with each other. While actually, for different sport items, their training scores are often correlated with each other [7][8][9][10] . For example, a player who swims well often has high lung vital capacity; and therefore, he or she is prone to get a higher training score when taking the running sport.…”

Section: Introductionmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

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With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

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BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

Cited by 5 publications

References 31 publications

Metabolite-disease association prediction algorithm combining DeepWalk and random forest

Metabolite-disease association prediction algorithm combining DeepWalk and random forest

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

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