Membrane protein expression can be predicted using statistical frameworks to provide an enhanced subset of sequences, out of an initial larger set of potentially expressing sequences, by using features derived from sequences and a model parameterized through a dataset of known expression levels. Also, membrane protein experimentation protocols can be designed using the statistical frameworks in concert known outcomes to identify which laboratory conditions are most likely to produce successful results.

A Fisher's exact test calculation apparatus includes: a condition storage 1 that has stored therein a condition for determining whether a result of Fisher's exact test corresponding to input is significant or not, the input being frequencies in a summary table; and a calculation unit 2 that obtains the result of Fisher's exact test corresponding to the frequencies in the summary table by inputting the frequencies in the summary table to the condition read from the condition storage 1.

A Fisher's exact test calculation apparatus includes: a condition storage 1 that has stored therein a condition for determining whether a result of Fisher's exact test corresponding to input is significant or not, the input being frequencies in a summary table; and a calculation unit 2 that obtains the result of Fisher's exact test corresponding to the frequencies in the summary table by inputting the frequencies in the summary table to the condition read from the condition storage 1.

Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.

Methods for producing an enriched reference data map useful for identifying critical factors for the development of a condition of interest are disclosed. The reference data map may be used to assess the risk or likelihood of a condition of interest being realized. In the context of medicine or genetics, the methods of the invention may be used to produce a risk assessment roadmap useful for identifying elements (biomolecular constructs, biological interactions, and biological pathways) that are critical to the development of a particular disease or syndrome. The roadmap may be consulted to design treatment methods having the greatest likelihood of successfully treating or preventing the development of a disease or syndrome. Also disclosed are methods for using such a risk assessment roadmap to evaluate a specific configuration of elements for determining the changes in the configuration of elements that will result in the achievement or the avoidance of a defined condition of interest. In the context of medicine or genetics, the invention provides methods for determining the susceptibility of an individual or group of individuals to develop a particular disease or syndrome utilizing biological data of the individual or group and assessing the level of risk by referencing a risk assessment roadmap prepared according to the disclosure herein. Uncertainty in diagnosis is minimized or eliminated by these methods, and the targets, interactions, and pathways most likely to be critical for disease development, and so representing the best intervention points for treatment or prevention of the disease or syndrome, are identified.

Methods for producing an enriched reference data map useful for identifying critical factors for the development of a condition of interest are disclosed. The reference data map may be used to assess the risk or likelihood of a condition of interest being realized. In the context of medicine or genetics, the methods of the invention may be used to produce a risk assessment roadmap useful for identifying elements (biomolecular constructs, biological interactions, and biological pathways) that are critical to the development of a particular disease or syndrome. The roadmap may be consulted to design treatment methods having the greatest likelihood of successfully treating or preventing the development of a disease or syndrome. Also disclosed are methods for using such a risk assessment roadmap to evaluate a specific configuration of elements for determining the changes in the configuration of elements that will result in the achievement or the avoidance of a defined condition of interest. In the context of medicine or genetics, the invention provides methods for determining the susceptibility of an individual or group of individuals to develop a particular disease or syndrome utilizing biological data of the individual or group and assessing the level of risk by referencing a risk assessment roadmap prepared according to the disclosure herein. Uncertainty in diagnosis is minimized or eliminated by these methods, and the targets, interactions, and pathways most likely to be critical for disease development, and so representing the best intervention points for treatment or prevention of the disease or syndrome, are identified.

Provided herein are methods of determining a molecular response score. The molecular response score may be used to monitor and guide administration of treatment to a subject.

Provided herein are methods of determining a molecular response score. The molecular response score may be used to monitor and guide administration of treatment to a subject.

A method of progressively training a base caller is disclosed. The method includes iteratively initially training a base caller with analyte comprising a single-oligo base sequence, and generating labelled training data using the initially trained base caller. At operations (i), the base caller is further trained with analyte comprising multi-oligo base sequences, and labelled training data is generated using the further trained base caller. Operations (i) are iteratively repeated to further train the base caller. In an example, during at least one iteration, a complexity of neural network configuration loaded within the base caller is increased. In an example, labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.

A method of progressively training a base caller is disclosed. The method includes iteratively initially training a base caller with analyte comprising a single-oligo base sequence, and generating labelled training data using the initially trained base caller. At operations (i), the base caller is further trained with analyte comprising multi-oligo base sequences, and labelled training data is generated using the further trained base caller. Operations (i) are iteratively repeated to further train the base caller. In an example, during at least one iteration, a complexity of neural network configuration loaded within the base caller is increased. In an example, labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.