A method for at least one of characterizing, diagnosing and treating a neurological health issue in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the neurological health issue based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the neurological health issue; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

To identify organism types from a target gene sequence, a server receives (S1) a target reference from a user via a telecommunications network. From a plurality of type-specific profiles, defining informative sequence regions for differentiating individual organisms, selected (S2) automatically is a profile having a highest correlation with the target gene sequence. The target gene sequence is compared (S4) automatically to reference sequences related to the selected profile. The comparison results related to the informative sequence regions are weighted (S5) and, from the reference sequences, determined (S9) is the organism type associated with the type-specific reference sequence, having a best match with the target gene sequence. The best match is determined based on the weighted comparison results. The profile search and weighted alignment provides identification of organism types from a target gene sequence while discriminating between trivial and significant inter-sequence differences.

In one embodiment, a method of encoding variation data for a population comprises receiving, by a variant encoding engine executing on a processor, information describing genetic variation of a population of individuals. The information comprises a plurality of variable sites within the reference genome of the population and the genotypes of a plurality of individuals in the population with respect to those variable sites. The method further comprises selecting an encoding strategy for the information based on the characteristics of the genetic variation across the population, and encoding the information according to the selected encoding strategy. In certain embodiments, selecting an encoding strategy may comprise determining the variability of a variable site within the population, and encoding information associated with the variable site based on the variability.

A method for constructing a library of amino-acid sequences having a common structural fold, and a method for designing and selecting an amino-acid sequence having a desired affinity to a molecular surface of interest of a molecular entity, using the library, are provided herein. The methods are based on a stochastic sampling of backbone conformations and amino acid conservation patterns observed in experimentally available protein structures having the common structural fold.

A method for at least one of characterizing, diagnosing and treating a neurological health issue in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the neurological health issue based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the neurological health issue; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

Drug combinations offer promising treatment for some conditions such as cancer. However, the large number of available drug combinations makes it impractical to try all possible combinations. Machine-learning techniques described in this disclosure train a classification algorithm. Once trained, the classification algorithm uses genomic data from a specific patient to perform in silico tests of drugs and drug combinations against the genomic data to determine which therapies are likely to be effective for treating a condition of the specific patient.

Techniques to identify a mechanism of action of a compound using network dysregulation are disclosed herein. An example method can include selecting at least a first interaction involving at least a first gene, determining a first n-dimensional probability density of gene expression levels for the first gene and one or more genes in a control state, determining a second n-dimensional probability density of gene expression levels for the first gene and one or more genes following treatment using at least one compound, estimating changes between the first probability density and the second probability density, and determining whether the estimated changes are statistically significant.

Embodiments are directed to a computer implemented method of assessing a relevancy of a drug to a disease state of a patient. The method includes assessing an impact of the drug on driver genes (DGs) of the disease state of the patient, assessing an impact of the drug on druggable target genes (DTs) of the drug, and assessing the relationship between the DGs and DTs that are in one of a plurality of biological pathways of the disease state of the patient. The method further includes combining the impact of the drug on the DGs, the impact of the drug on the DTs, and the relationship between the DGs and DTs that are in the one of the biological pathways, wherein the combining results in an assessment of the relevancy of the drug to the disease state of the patient.

A method for assessing the risk of an individual developing and Immunoglobulin-mediated reaction to one or more allergens increases the specificity of allergy diagnosis and evaluates the specificity of a given allergenic substance. The method may be utilized in in vitro allergy tests, apparatuses, and devices to increase the accuracy and precision of test results. A method to design and to evaluate the effects of personalized peptide molecules for IgE-antigen binding disruption is also presented.

Systems and methods are proposed for synthetic biology design and host cell simulation. In one form, a synthetic biology design system is proposed, comprising a model conversion component configured to: receive genetic circuit data indicative of a user-specified genetic circuit design; identify, from the genetic circuit data, constituent parts of the genetic circuit design, and the connections between the constituent parts; obtain mathematical models corresponding to the constituent parts; and combine the obtained mathematical models into a composite model configured to generate genetic circuit output data based on input data indicative of one or more of: free RNA polymerase concentration, free ribosome concentration and rRNA concentration. The system further comprises a host cell simulation component configured to receive, as input, the genetic circuit output data from the composite model, and based on the genetic circuit output data, generate host cell output data representing a physiological state of the host.