Drug repositioning method based on low-rank matrix completion

A low-rank matrix and repositioning technology, applied in the field of bioinformatics, can solve the problems of inapplicable drugs-target diseases-gene association data unavailable, etc.

Active Publication Date: 2018-01-05
深圳市早知道科技有限公司
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Problems solved by technology

However, these matrix factorization-based computational methods cannot be applied when drug-target and disease-gene association data are not available

Method used

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  • Drug repositioning method based on low-rank matrix completion
  • Drug repositioning method based on low-rank matrix completion
  • Drug repositioning method based on low-rank matrix completion

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Embodiment Construction

[0040] Concrete realization process of the present invention is as follows:

[0041] In the standard dataset adopted, a drug set, a disease set, and known drug-disease associations were included, which were obtained from the dataset collected by Gottlieb et al. The similarity between drugs is calculated based on the SMILES chemical structure information of drug molecules; the similarity between diseases is calculated based on the phenotype information of the disease.

[0042] 1. Construction of drug-disease heterogeneous network

[0043] First, based on the similarity between drugs, the similarity between diseases, and known drug-disease associations, drug networks, disease networks, and drug-disease networks are constructed, respectively. In the drug network, the drug node set R={r 1 ,r 2 ,...,r m} represents m kinds of drugs, and the weight of the edge between two drug nodes is equal to the chemical structure similarity between these two drugs; in the disease network, th...

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Abstract

The invention discloses a drug repositioning method based on low-rank matrix completion. The method comprises the steps that firstly, relevant data of drugs and diseases is integrated to build a drug-disease heterogeneous network, wherein the elements in a matrix comprise drug pairs, disease pairs, known drug-disease pairs and unknown drug-disease pairs; secondly, the matrix is completed by usinga quick matrix completion algorithm, predicted values are assigned to the unknown drug-disease pairs, and according to the completed predicted values, new indications are predicted for all drugs. Themethod is simple and effective, and through the tests on a plurality of data sets and the comparison with other method, it is shown that the method has good predicting performance in drug repositioning.

Description

technical field [0001] The present invention relates to the field of bioinformatics and involves the use of computational methods to predict new indications for known drugs. Background technique [0002] New drug research and development is a long-term and costly process. Statistics show that it takes an average of 10 to 15 years for a new drug from research and development to marketing, costing more than 800 million US dollars. In recent years, the investment in drug research and development has been increasing. The global cost of new drug research and development reached 141 billion U.S. dollars in 2015 and is expected to reach 160 billion U.S. dollars in 2020. However, relative to the huge R&D investment, the approval rate of new drugs has not been particularly significantly improved. According to statistics from the Center for Drug Evaluation and Research of the US FDA, from 1999 to 2004, the US FDA approved an average of 26 new drugs per year. The average number of ne...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/16G06F19/00
Inventor 王建新罗慧敏李敏刘锦卢诚谦
Owner 深圳市早知道科技有限公司
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