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Approximation spectral clustering algorithm based method for predicting cancer metastasis and recurrence

A spectral clustering algorithm, a technology for cancer metastasis, applied in computing, special data processing applications, instruments, etc., which can solve problems such as unpredictability, subjectivity that is difficult to replicate, and different patient outcomes.

Inactive Publication Date: 2016-01-13
HEFEI UNIV OF TECH
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Problems solved by technology

[0004] The TNM staging is used to judge whether cancer patients will have metastasis or recurrence, but the prognosis is not good; in actual treatment, doctors rely on their own experience to determine the treatment plan for cancer patients, which is highly subjective and difficult to replicate, and is unpredictable. Moreover, the prognosis of patients varies

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  • Approximation spectral clustering algorithm based method for predicting cancer metastasis and recurrence
  • Approximation spectral clustering algorithm based method for predicting cancer metastasis and recurrence
  • Approximation spectral clustering algorithm based method for predicting cancer metastasis and recurrence

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Embodiment Construction

[0030] In this embodiment, the method for predicting cancer metastasis and recurrence based on the approximate spectral clustering algorithm is: based on the approximate spectral clustering algorithm, using cancer gene expression profile data as a training set sample to construct a cancer metastasis and recurrence prediction model; The prediction model is used to test the independent test set samples of cancer metastasis and recurrence, and cancer patients are divided into two types of patients: metastasis recurrence and non-metastasis recurrence.

[0031] In this embodiment, the spectral clustering algorithm based on approximation is carried out as follows:

[0032] (1) Calculate the similarity matrix between any two cancer samples in the cancer gene expression profile data;

[0033] The cancer sample refers to a vector whose columns are gene expression profile data; according to the Gaussian function Calculate the similarity value s between two cancer samples ij , with th...

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Abstract

The present invention discloses an approximation spectral clustering algorithm based method for predicting cancer metastasis and recurrence. The method is characterized by comprising: based on an approximation spectral clustering algorithm, constructing a cancer metastasis and recurrence prediction model by using cancer gene expression profile data as a training set sample; and using the prediction model for a test of a cancer metastasis and recurrence independent testing sample set, and classifying cancer patients into two types of patients: a metastasis and recurrence type and a non metastasis and recurrence type. According to the method provided by the present invention, it is predicted whether the cancer patients will be subjected to metastasis and recurrence, so that subsequent treatment of the cancer patients is more targeted.

Description

technical field [0001] The invention belongs to the field of bioinformatics, and more specifically relates to a method for predicting cancer metastasis and recurrence based on an approximate spectral clustering algorithm, using the classification results of the algorithm to formulate a follow-up cancer treatment plan to improve the survival rate of cancer patients . Background technique [0002] Cancer metastasis and recurrence is the most significant biological phenotype of malignant tumors, and it is also the primary factor affecting its prognosis. The metastasis and recurrence of cancer lead to the direct death of 90% cancer patients, and the process of metastasis and recurrence is a synergistic process of multi-factors, multi-stages and multi-gene changes. [0003] Cancer metastasis and recurrence is an early event in the occurrence of malignant tumors, and the dysregulated expression of oncogenes and tumor suppressor genes is the molecular basis of cancer occurrence an...

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Application Information

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IPC IPC(8): G06F19/24
Inventor 史明光
Owner HEFEI UNIV OF TECH
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