The present invention relates to a method for identifying a cell (group), a method comprising a cell stratifying process utilizing quantitative physical property data, a method for separating a cell (group) utilizing the cell stratifying process, a method for identifying a molecular marker that identifies a cell (group) utilizing the cell stratifying process, a method for culturing a cell (group) utilizing the cell stratifying process, a program for causing a computer to execute a step of identifying a cell (group) utilizing the cell stratifying process, and a system for analyzing, identifying, or separating a cell (group) utilizing the cell stratifying process.

The present invention relates to a method for identifying a cell (group), a method comprising a cell stratifying process utilizing quantitative physical property data, a method for separating a cell (group) utilizing the cell stratifying process, a method for identifying a molecular marker that identifies a cell (group) utilizing the cell stratifying process, a method for culturing a cell (group) utilizing the cell stratifying process, a program for causing a computer to execute a step of identifying a cell (group) utilizing the cell stratifying process, and a system for analyzing, identifying, or separating a cell (group) utilizing the cell stratifying process.

A log of molecular events experienced by a cell and timing indicators for those events are stored in existing polynucleotides through a process of creating a double strand break (“DSB”) in a polynucleotide and inserting a new polynucleotide sequence by repairing the DSB with homology directed repair (“HDR”). The presence, order, and number of new polynucleotide sequences provides a log of events and timing of those events. Cellular mechanisms for creating the DSB and/or repairing with HDR are regulated by intra- or extra-cellular signals. When the log is created in the DNA of a cell, the changes may be heritably passed to subsequent generations of the cell. A correlation between the cellular signals and sequence of inserted HDR templates allows for identification of events and the timing experienced by the cell.

The invention provides methods of preparation of lipoproteins from a biological sample, including HDL, LDL, Lp(a), IDL, and VLDL, for diagnostic purposes utilizing differential charged particle mobility analysis methods. Further provided are methods for analyzing the size distribution of lipoproteins by differential charged particle mobility, which lipoproteins are prepared by methods of the invention. Further provided are methods for assessing lipid-related health risk, cardiovascular condition, risk of cardiovascular disease, and responsiveness to a therapeutic intervention, which methods utilize lipoprotein size distributions determined by methods of the invention.

Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.

Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.

Systems and methods for assaying a test entity for a property, without measuring the property, are provided. Exemplary test entities include proteins, protein mixtures, and protein fragments. Measurements of first features in a respective subset of an N-dimensional space and of second features in a respective subset of an M-dimensional space, is obtained as training data for each reference in a plurality of reference entities. One or more of the second features is a metric for the target property. A subset of first features, or combinations thereof, is identified using feature selection. A model is trained on the subset of first features using the training data. Measurement values for the subset of first features for the test entity are applied to thereby obtaining a model value that is compared to model values obtained using measured values of the subset of first features from reference entities exhibiting the property.

Systems and methods for assaying a test entity for a property, without measuring the property, are provided. Exemplary test entities include proteins, protein mixtures, and protein fragments. Measurements of first features in a respective subset of an N-dimensional space and of second features in a respective subset of an M-dimensional space, is obtained as training data for each reference in a plurality of reference entities. One or more of the second features is a metric for the target property. A subset of first features, or combinations thereof, is identified using feature selection. A model is trained on the subset of first features using the training data. Measurement values for the subset of first features for the test entity are applied to thereby obtaining a model value that is compared to model values obtained using measured values of the subset of first features from reference entities exhibiting the property.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.