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An improved method for predicting miRNA-disease associations based on collaborative filtering

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

Active Publication Date: 2020-09-29
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • An improved method for predicting miRNA-disease associations based on collaborative filtering
  • An improved method for predicting miRNA-disease associations based on collaborative filtering
  • An improved method for predicting miRNA-disease associations based on collaborative filtering

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Embodiment 1

[0101] (1) Database analysis:

[0102] We performed global LOOCV, local LOOCV and FFCV with HMDD database to evaluate 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 in turn picked as a test sample, while others are used as training samples. All untested associations were used as candidate samples. In local LOOCV, test samples are ranked among candidate samples of the disease to which they belong, while in global LOOCV, all candidate samples are ranked. The prediction is considered true if the ranking of the test sample is not lower than the given threshold. If the ranking of a candidate sample is not lower than a given threshold, the prediction is considered a false positive. After prediction of all known associations in LOOCV or FFCV, receiver operating characteristic (ROC) curves were plotted by calculating true positiv...

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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 present invention relates to human life medical engineering, and particularly provides an improved miRNA-disease association prediction method based on collaborative filtering for the prediction and verification of diseases related to miRNA. [0002] technical background [0003] MicroRNAs (miRNAs) are a class of short endogenous non-coding RNAs that regulate the expression of target miRNAs by complementary pairing of base sequences. Since the miRNAs lin-4 and let-7 were first discovered in C. elegans, the number of identified miRNAs has continued to increase. The latest miRbase records 1881 human miRNAs. Many studies have extensively demonstrated the importance of miRNAs in various biological processes. Although the interaction mechanism between miRNAs and the impact on diseases are still in the early stage of outbreak, it is certain that miRNAs contain very rich information, and the mechanisms involved in regulating molecules are also diverse. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H50/50G16B40/20
Inventor 颜成钢俞灵慧刘炳涛施海南邵碧尧李志胜
Owner HANGZHOU DIANZI UNIV
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