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Cancer chemosensitivity prediction technique based on molecular subnet and random forest classifier

A technology of random forest classification and molecular subnetwork, applied in the field of bioinformatics, can solve the problem of great disparity between patients with the same TNM stage, and achieve the effect of improving the accuracy rate and the accuracy of prognosis judgment

Inactive Publication Date: 2015-04-29
HEFEI UNIV OF TECH
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Problems solved by technology

[0004] The traditional tumor TNM staging is still the standard for whether cancer patients receive chemotherapy, but the prognosis of patients with the same TNM staging is often very different

Method used

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  • Cancer chemosensitivity prediction technique based on molecular subnet and random forest classifier
  • Cancer chemosensitivity prediction technique based on molecular subnet and random forest classifier

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Embodiment Construction

[0035] The cancer chemotherapeutic sensitivity prediction method based on molecular subnet and random forest classifier of the present invention is:

[0036] Integrating tumor gene expression profile data, tumor mutation genome information and protein interaction group information, based on the restart random walk model, mining the molecular subnetwork of cancer-causing and tumor-suppressor genes to achieve feature extraction;

[0037] Taking the molecular subnetwork as the input feature, based on the biological expression profile data of cancer patients, designing a training model based on the random forest algorithm, using the training model for the test of an independent test set, and obtaining the evaluation result of the patient's chemotherapy sensitivity;

[0038] Tumor gene expression profile data refers to: tumor gene expression profile data obtained using the gene expression profile data platform;

[0039] Tumor mutation genome information refers to: based on the know...

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Abstract

The invention discloses a cancer chemosensitivity prediction technique based on a molecular subnet and a random forest classifier. The method is characterized by comprising the steps of fusing data of oncogene expression profile, information of tumor mutation genome information and information of protein-protein interaction group, and excavating carcinogenic and cancer suppressor gene molecular subnets to realize feature extraction; taking the feature extraction as an input feature, designing a training model based on a random forest algorithm, and using the training model to be used for the testing of an independent test set, so as to obtain a chemosensitivity assessment of a patient. If the method provided by the invention is used for screening patients with effective chemotherapy effects before chemotherapy, the method has a significant meaning on cancer therapy.

Description

technical field [0001] The invention belongs to the field of bioinformatics, and relates to a method for screening cancer recurrence and metastasis markers based on a random walk model, and more specifically relates to the construction of a subnet-based random forest classifier, which can be used to improve cancer chemotherapy sensitivity predict. Background technique [0002] Chemosensitivity refers to the sensitivity of cancer patients to chemotherapy drugs. There are great differences in the sensitivity of cancer patients to chemotherapy drugs among individuals, and some cancers are resistant to drugs. Moreover, certain drugs are not very selective for treating cancer, which affects the effect of chemotherapy. [0003] In the treatment of cancer patients, due to the existence of chemosensitivity, regardless of whether recurrence or metastasis occurs, whether to use postoperative adjuvant chemotherapy has always been controversial, and it has become an important issue in...

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Application Information

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