35 results about "Cancer classification" patented technology

The present invention provides a process for classification of cancers and tissues of origin through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA tree-based expression framework allows optimization of treatment, and determination of specific therapy.

Aspects of the invention relate to methods and compositions for characterizing or modulating the expression of metabolic mesenchymal genes. In some embodiments, methods for assessing the expression of metabolic mesenchymal genes and related gene signatures are provided that are useful for cancer classification, prognosis, diagnosis, or treatment selection.

A method for classifying a possible cancer from a magnetic resonance spectrographic (MRS) dataset includes extracting at least one feature from the MRS dataset as being identified with the possible cancer and embedding the extracted feature into a low dimensional space to form an embedded space. The method then clusters the embedded space into clusters representing a plurality of predetermined classes and spectrally decomposing the clusters to identify substantially significant independent metabolic signatures. The method then classifies the possible cancer as belong to one of at least two cancer classes based on the identified independent metabolic signatures.

An automatic classification method for distinguishing between indolent and clinically significant carcinoma using multiparametric MRI (mp-MRI) imaging is provided. By utilizing a convolutional neural network (CNN), which automatically extracts deep features, the hierarchical classification framework avoids deficiencies in current schemes in the art such as the need to provide handcrafted features predefined by a domain expert and the precise delineation of lesion boundaries by a human or computerized algorithm. This hierarchical classification framework is trained using previously acquired mp-MRI data with known cancer classification characteristics and the framework is applied to mp-MRI images of new patients to provide identification and computerized cancer classification results of a suspicious lesion.

Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.

A system for classifying a patient's cancer as belonging to one or more Cancer Modules of 1 of 15 different cancer types is provided. The Cancer Modules are useful to identify patient populations and individual patients demonstrating specific prognosis, risk of metastasis and/or recurrence, response or lack of response to drugs, and the like.

The invention discloses a method and system for predicting cancer. The method comprises the following steps: carrying out difference analysis on the gene expression profile data of a cancer patient and a normal person to obtain difference genes; analyzing the gene expression profile data of the cancer patient and the normal person based on weighted gene co-expression network analysis to obtain a hub gene; processing the gene expression profile data of the difference genes through a variational auto-encoder algorithm to obtain dimension reduction data; and with the gene expression profile dataof the hub gene and the dimension reduction data as classification features of a cancer classifier of a preset type, conducting accurate classification of cancer patients and normal people through thecancer classifier. According to the method and the system for predicting cancer, the gene expression profile data of the hub gene obtained by weighted gene co-expression network analysis and the dimension reduction data processed by the variational auto-encoder are jointly used as the classification characteristics of the cancer classifier, so the accuracy of the cancer classifier is effectivelyimproved, and the purpose of efficiently predicting cancer is achieved.

The invention provides a screen method and screen device for protein markers for gastric cancer classification, and application of a screened protein marker. The screen method comprises steps of screening proteins meeting retaining conditions from a protein expression mass spectrometry database formed by multiple samples, and using the proteins as an effective protein set; performing twice dimension reducing processing on the effective protein set in sequence, so as to obtain a dimension-reduced protein set; performing clustering analysis on the dimension-reduced protein set, so as to obtain different types of protein markers. In the method, protein markers with remarkable high expression in cancer samples are screened out from a protein mass spectrometry database comprising a large quantity of gastric cancer samples, gastric cancer falls into different types of protein markers according to a relationship between different protein markers and survival rates, and the markers of different types have remarkable differences, helping classify gastric cancers more accurately.

The present invention relates to methods of classifying cancer cells based on the presence, absence or level of a tyrosine kinase or a phosphorylated tyrosine kinase. The present invention also relates to methods of treating cancer using cancer classification. The present invention further relates to methods of determining the effectiveness of a treatment for cancer using cancer classification.

The present invention provides a malignant cancer adjunctively therapeutic method which includes the three combined therapeutic regimens: reductive ion natural therapy, large dose vitamin C intravenous injection, and functional nutritional special prescription therapy. The functional nutritional special prescription therapy includes basic nutritional formula and functional formula and can greatly alleviate the suffering of cancer patients. By repairing and activating normal human cell ability, the malignant cancer adjunctively therapeutic method controls and inhibits the further development of cancer cells, formulates targeted therapeutic regimens according to cancer classification, and finds a new hopeful road for cancer patients.

The invention discloses a feature gene selecting and cancer classifying method which at least comprises the following steps of establishing a logistic regression model according to a super-parameter set and a to-be-processed gene data set; according to maximum likelihood estimation and logarithmic operation, expressing the logistic regression model as a loss function; establishing an SCAD-Net resolving model; according to the loss function and the SCAD-Net resolving model, obtaining an SNL model; calculating an iteration updating operator of the SCAD-Net; according to the iteration updating operator, calculating a gene regression coefficient of the SNL model through a coordinate gradient descent method; and according to the gene regression coefficient, performing feature gene selection andcancer classification. The feature gene selecting and cancer classifying method can effectively improve feature gene selecting and cancer classifying accuracy, thereby facilitating disease researching.

Aspects of the invention include methods for preparing an enriched sequencing library. In some embodiments, the methods involve preparing a sequencing library that is enriched for AT-rich sequences. In certain embodiments, the methods involve determining a presence or an absence of cancer, determining a cancer stage, monitoring cancer progression, and/or determining a cancer classification in a subject by analyzing an enriched sequencing library.

The invention belongs to the field of biological information, and discloses a cancer classification and characteristic gene selection method, which comprises the following steps of: establishment of a primary learner: establishing T logistic regression models and a spark group lasso regularized loss function solving model corresponding to the T logistic regression models, and outputting a secondary learner training set; establishing a secondary learner: establishing a multi-response regression model and a loss function solving model corresponding to L1 regularization, and outputting a training set prediction result; and a prognosis feature selection model: establishing a prognosis feature selection SGL model. According to the cancer classification and feature gene selection method, the three standards of prediction, stabilization and selection are met, the accuracy and stability of the model on cancer classification prediction are improved through stacking integration, oncogenes and cancer-related genes are accurately selected, and the interpretability of the model is enhanced; gene and gene pathway priori knowledge are fused, and the accuracy of cancer classification and the effectiveness of feature selection are improved.

The invention provides methods for classifying a cancer as susceptible to transmembrane prostate androgen induced (TMEPAI)-directed therapies, and methods for treating such cancers. The field of the invention pertains generally to medicine, pathology and oncology. More particularly, it addresses the treatment of breast cancer, such as triple-negative breast cancer, using a transmembrane prostate androgen induced (TMEPAI)-directed therapy.

Methods for generating a sequencing library from a sample comprising a plurality of single-stranded DNA molecules are provided, along with methods of using the generated sequencing library for detecting cancer, determining cancer stage, monitoring cancer progression, and/or determining a cancer classification from a test sample obtained from a subject.

The invention discloses a cancer classification method based on an miRNAs expression profile and a natural language model. The cancer classification method comprises the following steps: preprocessing a cancer miRNA expression data set sample; constructing a neighbor miRNA frequency data table according to the expression data of the miRNA; traversing each miRNA expression sample, and respectively calculating the statistical probability of each sample in the cancer group and the normal group based on a statistical language model method; and constructing a classifier to predict the category of the to-be-detected sample according to the statistical probability characteristics of the to-be-detected sample in the cancer group and the normal group. The cancer classification method based on the natural language model has the advantages of efficient prediction and simple calculation. According to the method, the ranking sequence of the expression values of the miRNA molecules in the samples is emphatically considered, and the expression difference of the miRNA molecules among the samples does not need to be concerned. The method is not influenced by a genome sequencing batch effect, and has wide applicability to classification of cancer samples.

Biomarkers, particularly hypoxia-related genes, and methods using the biomarkers for molecular detection and classification of disease are provided.

The invention relates to the field of agricultural microorganisms, in particular to a rhizosphere growth-promoting bacterium and its application. The rhizosphere growth-promoting bacterium is the rhizosphere growth-promoting bacteria N10, and its classification is named Enterobacter cancerogenus (Enterobacter cancerogenus), which was preserved in the General Microbiology Center (CGMCC) of the China Microbiological Culture Collection Management Committee (CGMCC) on March 25, 2015. The number is CGMCC No.1.14969. The bacterial strain of the present invention has the growth-promoting characteristics of nitrogen fixation, phosphorus dissolution, and indole acetic acid production, can increase soil nutrients, improve the effectiveness of fertilizers, and promote the absorption of soil nutrients by plants. Good prospects. The invention has a remarkable growth-promoting effect on wheat, and the good growth-promoting characteristics of the bacterial strain promote the growth of the aboveground parts of wheat.