Abstract: Disclosed are techniques for characterizing variants of interest and predicting assignments of individuals to communities based on obtained genetic information. To characterize a variant, DNA datasets of reference individuals are accessed and used to generate a cluster with additional individuals. Reference individuals carry a variant at a genetic locus and the additional individuals share IBD with reference individuals. Statistics of genealogical data of the cluster are generated. A result summarizing the characterization of the variant is generated based on the statistics. To determine if an individual belongs to a community, a subset of the individual's haplotypes are inputted into a community-specific model. The model is trained using the training samples that each include haplotypes of reference individuals and a label identifying whether the reference individual belongs to the community. Based on the output of the model, it is determined whether the individual is a member of the community.

Abstract: Methods of associating a test compound with a compound property. One or more datasets including: for each of a plurality of cell lines and each of a plurality of compounds: for each respective exposure condition: a corresponding response signature for the respective compound in the respective cell line under the respective exposure condition is obtained. A correlation is determined for each unique combination of exposure conditions for a respective pair of compounds based on the corresponding response signature. A weight is determined for each respective pair of compounds based on the determined correlations. A plurality of compound clusters is formed, where each cluster represents compounds that satisfy one or more weight criteria with respect to a particular compound. A compound property of the test compound is determined from properties of one or more compounds in a one or more compound clusters that contain the test compound.

Abstract: Retrosynthetic methods are described for determining one or more optimal synthetic routes to generate a target compound.

Abstract: In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Abstract: Techniques for predicting a pair of an enzyme primary sequence and a substrate, and interaction probability for the pair are described. An exemplary method includes receiving a request to predict a pair of an enzyme primary sequence and a substrate, and interaction probability for the pair; combining an enzyme vector, a substrate vector, and an interaction indication for the enzyme and substrate to form a machine learning model input; applying a machine learning model to the machine learning model input to predict the pair of an enzyme primary sequence and a substrate, and interaction probability for the pair; and outputting a result of the application of the machine learning model including the predicted pair of an enzyme primary sequence and a substrate, and interaction probability for the pair.

Abstract: Methods, apparatuses, and systems for analyzing complete microorganism strains in complex heterogeneous communities, determining functional relationships and interactions thereof, and synthesizing bioreactive modificators based thereon are disclosed. Methods for identifying and utilizing pathways and functionalities of interest based thereon are also disclosed.

Abstract: A method for analyzing sequences performed by one or more processors is provided, including aligning first sequence data generated at a first time point based on reference sequence data, in which the first time point is a time point after reading of a first read of a pair of paired-end reads is completed and at which a second read of the pair of paired-end reads is partially read, identifying a structural variant from the aligned first sequence data, and before reading the second read is completed, generating a first report including information on the identified structural variant.

Abstract: Systems and methods for generating a surgical plan for altering an abnormal bone using a generic normal bone model are discussed. For example, a system for planning a surgery on an abnormal bone can include a model receiver module configured to receive a generic normal bone model. The generic normal bone model, such as a parametric model derived from statistical shape data, can include a data set representing a normal bone having an anatomical origin comparable to the abnormal bone. An input interface can be configured to receive an abnormal bone representation including a data set representing the abnormal bone. A surgical planning module can include a registration module configured to register the generic normal bone model to the abnormal bone representation by creating a registered generic model. A surgical plan formation module can be configured to identify one or more abnormal regions of the abnormal bone using the registered generic model.

Abstract: A device including: defining unit to define lattice space that is collection of lattices compound groups are sequentially arranged; limiting unit to, in the case where any of the compound groups is arranged in any of the lattices of lattice space followed by arranging next compound group in the lattice space, generate limited lattice space created by eliminating, from the lattice space, undesirable regions for the next compound group to be arranged; and assigning unit to assign bit to each of lattice points, to which the compound groups can be arranged, in the limited lattice space; and an arithmetic unit configured to perform ground state search on Ising model obtained through conversion based on restriction conditions related to each lattice point according to simulated annealing, to calculate minimum energy of the Ising model, wherein the device is for searching compound in which the compound groups are linked with one another.

Abstract: The method for protein selection can include: characterizing a protein set, training a prediction model, determining target characteristic values, and determining a candidate protein set based on the target characteristic values.

Abstract: Provided herein are methods implemented by a processor in a computer for designing a clamp peptide comprising the structure A1-B-A2 wherein A1 is a first peptide arm that binds to a first binding site of a target protein, A2 is a second peptide arm that binds to a second binding site of the target protein, and B is a bridge peptide which links A1 to A2. Also provided herein are computer-readable storage media having stored thereon machine-readable instructions executable by a processor and systems. Related methods of manufacturing a clamp peptide and the clamp peptides manufactures by the methods are provided.

Abstract: Apparatus and methods are described including using a sensor to monitor a sleeping subject and generate a signal in response to the monitoring. A control unit is used to accept an input indicative of a person desiring to perform an activity that is potentially disturbing to the sleep of the subject, analyze the sensor signal, in response to the analyzing, identify a time during which the activity is likely to be less disturbing to the sleep of the subject, relative to another time, and generate a notification indicating a suitability of performing the activity at the identified time. Other applications are also described.

Abstract: Methods are disclosed for reducing the size of a k-mer reference database used for queries and/or taxonomic classifications when available computer storage and/or memory are inadequate. The k-mers of the reference database have been previously classified to a taxonomy, preferably based on genetic distances. In one method, the k-mers are separated into one or more groups followed by removing k-mers common to the groups. In another method, k-mers are removed based on a selected taxonomic threshold level. A third method combines the features of the previous two methods. The methods are adaptable to machine learning.

Abstract: Methods are provided for the rational design of stable communities of microbes for benefiting the health of a host organism, including human and/or animal health. The methods describe design of microbial consortia based on providing and/or complementing key functionalities lacking or underrepresented in the microbiome of an organism having a disorder or disease as compared to healthy subjects. The consortia are designed to possess metabolic interdependencies for improved engrafting, stability and performance of the consortium. Compositions that include the designed microbial consortia are provided for treatment of disorders/diseases involving chronic inflammation, infection, and the combination of chronic inflammation and infection including inflammatory bowel disease and related disorders. The compositions are also broadly applicable for the treatment of neurological, metabolic and oncology-related conditions.

Abstract: Grouping vectors describing functional interactions between compounds and reagents into clusters identifies groups of similar compounds, and may identify drug targets, pathways of action, deleterious toxicities, immune stimulation and evasion potential, or other information regarding interactions between compounds and reagents.

Abstract: A bioinformatics method is provided for identifying candidate biological sequences, such as DNA, RNA, and proteins, with high sensitivity and specificity for application in procedures such as PCR and gene and protein sequencing. The method involves categorizing a collection of biological sequences within an out-group and an in-group, identifying the intersection between the in-group and the out-group, the union of the out-group, and a relative complement of sequences that are members of the in-group, but not the out-group. A biological signature for a species of interest with high sensitivity and specificity will be a member of the relative complement that has an out-group frequency of zero.

Abstract: An analytics system marks duplicate fragments from an initial set of fragments from a subject. The analytics system generates a sample state vector for each fragment. Each sample state vector comprises a sample genomic location within a reference genome and a plurality of methylation states for a plurality of CpG sites in the fragment, the methylation states determined to be one of methylated, unmethylated, variant, and ambiguous. The analytics system identifies two fragments with methylation state vectors as being derived from a matching reference location, e.g., sharing a common plurality of CpG sites. The analytics system calculates a modified Hamming distance based on methylation states in the first sample state vector and methylation states in the second sample state vector. Based on the modified Hamming distance, the analytics system marks the first fragment and the second fragment as either duplicate fragments or non-duplicate fragments.

Abstract: A system to estimate consequences of an explosion includes a scenario generator to generate a scenario based on user input and scenario parameters; an explosive device model generator to generate an explosive device model based on the user input and explosive device parameters; a propagation of hazards modeler to model propagation of hazards into the scenario based on the user input and hazard parameters; an injury modeler to model injuries corresponding to modeling propagation of hazards into the scenario based on injury parameters; and an iteration and output generator to iterate by using additional iterative subsets of the parameters to generate the scenario, generate the explosive device model, model propagation of hazards, and model injuries, until parameter spaces of the parameters are covered. The iteration and output generator generates an injury record based injury outcomes from the modeling of injuries corresponding to the iterating.

Abstract: Methods predict candidate epitopes in a protein by: providing a conformational sampling engine; obtaining a model of a protein suitable for use with the conformational sampling engine and comprising a native structure; applying a collective coordinate bias to the protein model, the collective coordinate bias causing the conformational sampling engine to in turn force the protein model to at least partially unfold from its native structure to an updated structure, wherein the collective coordinate bias is applied globally to at least a substantial portion of the protein model and is impartial as to where, within the substantial portion of the protein model, unfolding occurs; and analyzing the updated structure to identify one or more candidate epitopes, where the one or more candidate epitopes exhibit indicia of localized unfolding.

Abstract: Analysis of the flow of fluxes through the metabolic network of a cell type is useful in gaining knowledge about cellular physiology. Such knowledge can be used in understanding host's interactions with pathogens, drug response etc. However there is dearth of techniques that can incorporate metabolomics data into genome scale metabolic model (GEM) for FBA. A method and system for analyzing metabolic state of a cell at a genome scale by measuring concentrations of a one or more metabolites has been disclosed. The method is utilizing intracellular and/or extracellular metabolite concentrations for constraining reaction fluxes in FBA by incorporating it as part of stoichiometric constraint to metabolic model. The method is used to predict the change in flux flow through all reactions in an organism/cell type under different experimental conditions. The method enables constraining the flow of fluxes through reactions while performing FBA of GEMs using measured metabolite concentrations.