The present invention relates to a method of analyzing a corrected TMB and a method for predicting a response to immune checkpoint inhibitors in a cancer patient using the same. According to a method, a computer-readable recording medium and an analyzing apparatus, for providing information according to an aspect, since the corrected TMB of the present invention is markedly highly predictive of the response to cancer immunotherapy in the cancer patient, compared to the conventional TMB, a patient group predicted to show a therapeutic effect can be selected and an appropriate treatment can be administered, thereby alleviating pain and treatment costs from the cancer patient.

Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.

Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.

Techniques related to the detection or identification of biological cells or substances.

Techniques related to the detection or identification of biological cells or substances.

Disclosed is a method for calculating genetic score based on genotypic information that predicts plasticity in a phenotype. More particularly, disclosed is an algorithm to calculate a particular type of genetic score based on basic genotypic information that is provided by commercially available technologies ranging from “SNP-chips” to whole genome sequencing. Polygenic scores— attempts to summarize the genetic propensity for or risk of a given phenotype (i.e., disease or trait)—have been around for more than a decade. They aim to predict the level of a trait—i.e., how tall or short someone may be or what their blood pressure or BMI might be. The Variation Polygenic Score (“vPGS”) disclosed herein is different. Its purpose is not to predict whether someone who scores higher or lower on the vPGS will be, for instance, heavier or lighter or have a higher or lower IQ. Rather, it is formulated to predict variation. The disclosed vPGS does not predict the mean level but rather the dispersion around that mean. It likely also predicts individual changes in a phenotype over the lifecourse (e.g., whether an individual tends to fluctuate greatly in weight). The disclosed approach is very suited for gene-environment interaction studies: that is, it is a good measure of the genetic propensity to be influenced by the environment for or intervention on a particular trait, disease or other phenotype.

Disclosed is a method for calculating genetic score based on genotypic information that predicts plasticity in a phenotype. More particularly, disclosed is an algorithm to calculate a particular type of genetic score based on basic genotypic information that is provided by commercially available technologies ranging from “SNP-chips” to whole genome sequencing. Polygenic scores— attempts to summarize the genetic propensity for or risk of a given phenotype (i.e., disease or trait)—have been around for more than a decade. They aim to predict the level of a trait—i.e., how tall or short someone may be or what their blood pressure or BMI might be. The Variation Polygenic Score (“vPGS”) disclosed herein is different. Its purpose is not to predict whether someone who scores higher or lower on the vPGS will be, for instance, heavier or lighter or have a higher or lower IQ. Rather, it is formulated to predict variation. The disclosed vPGS does not predict the mean level but rather the dispersion around that mean. It likely also predicts individual changes in a phenotype over the lifecourse (e.g., whether an individual tends to fluctuate greatly in weight). The disclosed approach is very suited for gene-environment interaction studies: that is, it is a good measure of the genetic propensity to be influenced by the environment for or intervention on a particular trait, disease or other phenotype.

This disclosure describes an efficient method to copy all polynucleotides encoding digital data of digital files in a polynucleotide storage container while maintaining random access capabilities over a collection of files or data items in the container. The disclosure further describes a process whereby random-access and sequencing of the polynucleotides are combined in a single step.

This disclosure describes an efficient method to copy all polynucleotides encoding digital data of digital files in a polynucleotide storage container while maintaining random access capabilities over a collection of files or data items in the container. The disclosure further describes a process whereby random-access and sequencing of the polynucleotides are combined in a single step.

A method of using a sequencing cell includes applying voltage across the sequencing cell, acquiring one or more signal values from the sequencing cell, and acquiring one or more correlated signal values that are correlated with respective values of the plurality of acquired signal values thereby forming a plurality of two-dimensional data points. The plurality of two-dimensional data points comprise values in a first dimension that equal the plurality of acquired signal value and values in a second dimension that equal the plurality of correlated signal values. The method can further include computing a plurality of transformed signal values by applying a two-dimensional transformation to the plurality of two-dimensional data points.