Abstract: The present invention relates to a method for measuring a mutation rate, comprising preparing a library for next generation sequencing (NGS). Accordingly, the present invention can measure the effect of drugs, radiation, gene composition, aging, and various stresses experienced by an individual, etc. on the occurrence of mutations in a subject sample, and thus can be used for testing, diagnosis, management and evaluation related to toxicity tests, medical tests, maintenance and management of health, etc.

Abstract: A method for identifying two or more infections as related or non-related infections based on an estimated genetic relatedness of the two or more infections, comprising: (i) receiving, for each of two or more infected patients, infection-relevant information comprising an antibiotic resistance profile for the patient's infection, a geo-temporal record for the patient, and a caregiver history for the patient; (ii) estimating, using a trained genetic relatedness model, a genetic relatedness of at least two of the two or more infections; (iii) comparing the estimated genetic relatedness between at least two of the two or more infections to a predetermined threshold; (iv) identifying, based on the comparison, the at least two of the two or more infections as a related infection or a non-related infection.

Abstract: A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.

Abstract: Contemplated systems and methods use high-accuracy in silico HLA analysis to so establish a transplant match database suitable for transplantation, and particularly stem cell and solid organ transplant.

Abstract: Computer-implemented processes for precision therapeutic biomarker screening for cancer include using a model-based overlapping clustering framework to assess large numbers of possible drugs and drug combinations against patient data, including cell line responsiveness. A multivariate regression model has been developed, along with a latent overlapping cluster indicator variable. The techniques employ a new finite mixture of multivariate regression (FMMR) model and expectation-maximization (EM) algorithm for modeling. The techniques can analyze large amounts of drug data and identify complex overlapping drug clusters, as well as cluster-wise drivers that facilitate identification of new drugs for treating pathologies, such as cancer, in patients.

Abstract: Treating colorectal cancer in a stage IV uses a systemic chemotherapy, either a FOLFOX regimen or a FOLFIRI regimen. However, a method of predicting which of the regimens is effective to an individual patient would be advantageous. Provided is a biomarker that indicates which of the regimens, the FOLFOX regimen or the FOLFIRI regimen, is advantageous to select for a patient having colorectal cancer. The biomarker is characterized by being a gain in the copy number of at least one region on human chromosomes among 7p15.3, 7q34, 8q24.1, 8q24.2, 8q24.1-q24.2, 9q34.3, 13q12.2, 13q14.11, 13q22.1, 13q32.2-q32.3, 13q34, 20q12, 20q13.13, 20q13.2, and 20q13.3. Using such a biomarker enables to determine which, the FOLFOX regimen or the FOLFIRI regimen, is more advantageously selected.

Abstract: Described herein are systems and methods for designing and testing custom biologic molecules in silico which are useful, for example, for the treatment, prevention, and diagnosis of disease. In particular, in certain embodiments, the biomolecule engineering technologies described herein employ artificial intelligence (AI) software modules to accurately predict performance of candidate biomolecules and/or portions thereof with respect to particular design criteria. In certain embodiments, the AI-powered modules described herein determine performance scores with respect to design criteria such as binding to a particular target. AI-computed performance scores may, for example, be used as objective functions for computer implemented optimization routines that efficiently search a landscape of potential protein backbone orientations and binding interface amino-acid sequences.

Abstract: An iterative process for optimizing one or more parameters used by a k-mer based de novo genome assembler program to assemble a set of sequenced nucleic acids is described. The method utilizes quality metrics whose desired values are initially specified. Computed values of the quality metrics are calculated during the assembly process and compared to the desired values. The assembly process stops when the computed values are not desired values. After modification of one or more of the parameters (e.g., k-mer value), the assembly process re-initiates using the modified parameter set. This process repeats until the computed values of the quality metrics meet the desired values. The final parameter set is then used to generate or complete one or more final assembled genomes.

Abstract: The invention relates to a method for the reclassification of a subject to a more appropriate risk assessment to that obtained using the algorithms for such risk estimation such us but not limited to Framingham, Regicor, Score, Procamor Qrisk based on the presence of different polymorphisms. The invention also relates to a method for determining the risk of suffering a cardiovascular disease by combining the absence or presence of one or more polymorphic markers in a sample from the subject with conventional risk factors for CVD as well as computer-implemented means for carrying out said method.

Abstract: A system for identifying and ameliorating body degradations, the system comprising a computing device, wherein the computing device is configured to receive biological extraction data. Computing device may generate, as a function of a degradation machine-learning model and the biological extraction data, a degradation profile. Computing device may calculate a biological degradation function that is a mathematical function that describes the change in rate of degradation over time corresponding to the user. Computing device may identify, using a degradation imbalance machine-learning process and the degradation profile, a degradation imbalance. Computing device may determine, as a function of the degradation imbalance machine-learning process and the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of a user by performing a simulation.

Abstract: A platform and method for enabling collaboration on data analysis of life sciences data across disparate databases are disclosed. The collaboration platform may allow for performing exploratory analysis for drug discovery and development. The collaboration platform may include a search and graph module for generating a user project and determining and displaying one or more matching data assets and one or more potential collaborators; a collaboration module for coordinating a collaboration between the user and one or more selected collaborators; a data management module for receiving a schema for one or more producer projects, receiving data from the one or more selected data assets, and ingesting the received data using common standards and an ontology; and an insight application for generating disease specific inferences relating to a scientific question using the ingested received data, and receiving a feedback from the user and/or the selected collaborators to improve the search and graph module.

Abstract: Executing workflows can include receiving, via an interface associated with execution of a workflow, an entry corresponding to a particular workflow object from among a plurality of workflow objects of a datacenter, communicating the entry to a third-party system, receiving, from the third-party system, search results corresponding to the entry, wherein the search results include indications of a subset of the plurality of workflow objects of the datacenter that each have a threshold correlation with the entry, displaying, via the interface, a plurality of items respectively corresponding to the subset of the plurality of workflow objects of the search results, receiving a selection, via the interface, of an item of the plurality of items, and designating a workflow object of the subset of the plurality of workflow objects of the search results that corresponds to the selected item as an input parameter of the workflow responsive to the selection.

Abstract: Systems and methods are disclosed for generating a therapeutic response predict or detecting a disease, by: using a genetic analyzer to generate genetic information; receiving into computer memory a training dataset comprising, for each of a plurality of individuals having a disease, (1) genetic information from the individual generated at first time point and (2) treatment response of the individual to one or more therapeutic interventions determined at a second, later, time point; and implementing a machine learning algorithm using the dataset to generate at least one computer implemented classification algorithm, wherein the classification algorithm, based on genetic information from a subject, predicts therapeutic response of the subject to a therapeutic intervention.

Abstract: An apparatus, computer program product, and method are provided for the determination of one or more components of an EC number through the application of a level-by-level modeling approach capable of conducting feature reconstruction and classifier training simultaneously, based on encoded aspects of a sequence listing for a protein with an unknown function. The method includes receiving a sequence source data object associated with an enzyme; extracting a sequence data set from the sequence source data object; encoding the sequence data set into a first and second encoded sequence; generating a first predicted characteristic of the enzyme by applying the first and second encoded sequence to a first level of a model comprising a plurality of levels; and generating a second predicted characteristic of the enzyme by applying the first and the second encoded sequences to a second level of the model comprising a plurality of levels.

Abstract: A new snapshot of a storage volume is created by instructing computing nodes to suppress write requests. Once pending write requests from the computing nodes are completed, storage nodes create a new snapshot for the storage volume by allocating a new segment to the new snapshot and finalizes and performs garbage collection with respect to segments allocated to the previous snapshot. Subsequent write requests to the storage volume are then performed on the segments allocated to the new snapshot. An orchestration layer implements a multi-role application that is provisioned with virtualized storage and computation resources. A snapshot of the application may be created and used to rollback or clone the application. Clone snapshots of storage volumes may be gradually populated with data from prior snapshots to reduce loading on a primary snapshot.

Abstract: Presented herein are systems and methods for prediction of protein interfaces for binding to target molecules. In certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule, such as another protein or peptide. In certain embodiments, graph-based neural network models described herein may receive, as input, a representation (e.g., a graph representation) of a complex comprising a target and a partially-defined custom biologic. Portions of the partially-defined custom biologic may be known, while other portions, such an amino acid sequence and/or particular amino acid types at certain locations of an interface, are unknown and/or to be customized for binding to a particular target.

Abstract: A technique for learning and steering evolutionary dynamics may include initializing a bioreactor including a population of evolving organisms; determining selection pressures; (a) applying the selection pressures to the population; (b) determining the population state and storing it in a population dataset; (c) detecting whether the population has reached a stable state; (d) if the population has reached the stable state: obtaining data representing the stable state, redetermining the selection pressures based on a selection pressure policy, and storing the data and the redetermined selection pressures in a stable state dataset; (e) determining whether one or more stopping criteria have been met; and repeating steps (a)-(e) until at least one of the stopping criteria is met.

Abstract: Methods of identifying changes in genomic DNA copy number are disclosed. This disclosure provides methods for detecting chromosomal aberrations in a subject using Hidden Markov modeling. In some cases, methods provided herein use de novo sequence assembly to detect chromosomal aberrations in a subject. The methods can be used to detect copy number changes in cancerous tissue compared to normal tissue. The methods can be used to diagnose cancer and other diseases associated with chromosomal anomalies.

Abstract: Methods and systems are provided for accurate and efficient identification and quantification of proteins. In an aspect, disclosed herein is a method for identifying a protein in a sample of unknown proteins, comprising receiving information of a plurality of empirical measurements performed on the unknown proteins; comparing the information of empirical measurements against a database comprising a plurality of protein sequences, each protein sequence corresponding to a candidate protein among a plurality of candidate proteins; and for each of one or more of the plurality of candidate proteins, generating a probability that the candidate protein generates the information of empirical measurements, a probability that the plurality of empirical measurements is not observed given that the candidate protein is present in the sample, or a probability that the candidate protein is present in the sample; based on the comparison of the information of empirical measurements against the database.

Abstract: Trigger based analytics database synchronization is described. In one example case, a trigger is invoked based on an operation issued for a record in a transactional database. According to the trigger, one or more data values for synchronization from the transactional database to an analytics database are determined. A message including the data values is formed and added to a message queue through a message infrastructure service including a message broker. In turn, the values from the message are stored in a suitable memory space, such as a staging table, for forwarding to an analytics computing system. Using the trigger and the message infrastructure service, execution of the transactional database operation can be detached in execution from the addition of the value to the staging table and synchronization with the analytics computing system.