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Improved miRNA-disease relevance prediction method based on collaborative filtering

A technology of collaborative filtering and prediction method, which is applied in the field of human life and medical engineering, and can solve the problem that the parameter K is not easy to choose.

Active Publication Date: 2017-12-22
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Another limitation of HDMP is that its parameter K is not easy to choose, and different K will be chosen for different diseases

Method used

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  • Improved miRNA-disease relevance prediction method based on collaborative filtering
  • Improved miRNA-disease relevance prediction method based on collaborative filtering
  • Improved miRNA-disease relevance prediction method based on collaborative filtering

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Embodiment 1

[0100] (1) Database analysis:

[0101] We performed global LOOCV, local LOOCV and FFCV with the HMDD database for evaluation on ICFMDA. To evaluate the performance of ICFMDA, we use five state-of-the-art methods for comparison. They are HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA. In LOOCV, each known miRNA-disease association is sequentially picked as a test sample, while others are used as training samples. All untested associations are used as candidate samples. In local LOOCV, test samples are ranked among candidate samples for the disease they belong to, while in global LOOCV, all candidate samples are ranked. If the ranking of the test sample is not lower than the given threshold, the prediction is considered true. If the candidate sample's rank is not lower than a given threshold, the prediction is considered a false positive. Receiver operating characteristic (ROC) curves were plotted by calculating the true positive rate (TPR) versus the false positive rate (FPR) at ...

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Abstract

The invention discloses an improved miRNA-disease relevance prediction method based on collaborative filtering. A miRNA-disease prediction problem can be regarded as a recommendation repair problem. On the basis of a known miRNA-disease-related bipartite network, miRNAs are recommended to use according to known preferences of the miRNAs to related diseases and vice versa. Firstly, an importance matrix SIGd of one disease to another is defined, calculated and measured. When a disease d (i) is perceived to be more important than a disease d (j), the score of SIGd (d(i), d(j)) is higher. Similarly, SIGr is defined and calculated in order to measure the importance of two miRNAs. Secondly, a significant matrix and a similarity matrix are utilized as weight for calculating scores. The similarity matrix is defined to represent similarity between miRNAs or between diseases. The final score of miRNA-disease relevance is the sum of the scores of a miRNA and a disease and score of the miRNA scored by the disease. With the method, higher prediction accuracy is realized.

Description

technical field [0001] The invention relates to human life medical engineering, especially for disease prediction and verification work related to miRNA, and provides an improved miRNA-disease association prediction method based on collaborative filtering. technical background [0002] MicroRNA (miRNA) is a type of short endogenous non-coding RNA that regulates the expression of target miRNA through complementary pairing of base sequences. Since the miRNAs lin-4 and let-7 were first identified in C. elegans, the number of identified miRNAs has continued to increase. The latest miRbase records 1881 human miRNAs. The importance of miRNAs in various biological processes has been extensively demonstrated by many studies. Although the interaction mechanism between miRNAs and the impact on diseases are still in the early stage of the outbreak, it is certain that the information contained in miRNAs is very rich, and the mechanisms involved in regulating molecules are also diverse...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 俞灵慧颜成钢刘炳涛施海南邵碧尧李志胜
Owner HANGZHOU DIANZI UNIV
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