An example method of treating a subject having a tumor is described herein. The method can include determining a radiosensitivity index of the tumor, deriving a subject-specific variable based on the radiosensitivity index, and obtaining a genomic adjusted radiation dose effect value for the tumor. The radiosensitivity index can be assigned from expression levels of signature genes of a cell of the tumor. Additionally, the genomic adjusted radiation dose effect value can be predictive of tumor recurrence in the subject after treatment. The method can also include determining a radiation dose based on the subject-specific variable and the genomic adjusted radiation dose effect value.

There is provided a technology that supports selection of a label to be used for analysis of target molecules. The present technology provides a label selection support system including an information acquisition unit that obtains, via a network, information associated with a plurality of target molecules to be analyzed, an information processor that obtains, using the information associated with a plurality of target molecules, in vivo expression information of the plurality of target molecules from a database storing in vivo expression information of target molecules and generates support information associated with assignment of a label to each of the plurality of target molecules on the basis of the expression information, and a transmitter that transmits the generated support information via the network.

There is provided a technology that supports selection of a label to be used for analysis of target molecules. The present technology provides a label selection support system including an information acquisition unit that obtains, via a network, information associated with a plurality of target molecules to be analyzed, an information processor that obtains, using the information associated with a plurality of target molecules, in vivo expression information of the plurality of target molecules from a database storing in vivo expression information of target molecules and generates support information associated with assignment of a label to each of the plurality of target molecules on the basis of the expression information, and a transmitter that transmits the generated support information via the network.

Products, systems, and methods for classifying human colorectal cancer into a consensus molecular subtype (CMS) and for assessing risk of recurrence based on CMS scores and based on risk scores derived from abbreviated gene expression profiles, for determining suitable treatment protocols for human colorectal cancer patients based on the determined CMS classification and based on the determined risk of recurrence, and for administering the suitable treatment protocols.

Products, systems, and methods for classifying human colorectal cancer into a consensus molecular subtype (CMS) and for assessing risk of recurrence based on CMS scores and based on risk scores derived from abbreviated gene expression profiles, for determining suitable treatment protocols for human colorectal cancer patients based on the determined CMS classification and based on the determined risk of recurrence, and for administering the suitable treatment protocols.

Disclosed herein are a method for analyzing a genetic interaction to reduce a false positive in gene screening for at least one gene cluster associated with at least one type of cells by deriving the genetic interaction and a synthetic partner with at least one profile selected from the group consisting of a mutation profile, a loss-of-function profile, and an expression profile; and a system using same.

Disclosed herein are a method for analyzing a genetic interaction to reduce a false positive in gene screening for at least one gene cluster associated with at least one type of cells by deriving the genetic interaction and a synthetic partner with at least one profile selected from the group consisting of a mutation profile, a loss-of-function profile, and an expression profile; and a system using same.

A computer-implemented method and system uses a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas. The method and system: (A) use the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each training tumor-biological component pair, a corresponding biological component score; and (B) construct, based on the two atlases and the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on its gene expression data. The method and system may generate the map before using it. The method and system may apply the machine learning perceptron classifier to the input tumor's gene expression data to generate the tumor type of the input tumor.

The present invention relates to methods for the classification and stratification of cells within tumours. In one aspect, the invention provides methods for classifying cancer cells into intrinsic cancer subtypes, as well as for diagnosing, prognosing and evaluating a response to therapy for patients afflicted with cancer.

A novel classification strategy is described for forecasting clinical outcomes of Diffuse Large B-cell Lymphoma using targeted RNA sequencing combined with machine learning algorithms. The novel method classifies subjects with DLBCL into subgroups based on the clinical course of their disease and expected survival, rather than on Cell of Origin. To focus on survival, the methods first deploy machine learning and divide the subjects into subgroups based on their overall survival. A modified Bayesian classifier is then used to select genes that can forecast various survival groups, followed by validation of these biomarkers using an independent set of clinical cases. This novel approach for stratifying subjects with DLBCL based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy can be used to select high responders and low responders to R-CHOP. Low responders may be offered additional or alternative therapies to improve their survival.