1450 results about "Bio informatics" patented technology

The present invention is based on the encoding of sequential data or sequences in a novel manner that permits efficient storage and processing of sequential data, as well as methods for searching sequences or databases of sequences. The methods and systems of the current invention may be adapted broadly to various fields of application and to a variety of sequences types. For example, the current invention has broad application including to the fields of bioinformatics, molecular biology, pharmacogenomics, phonetic sequences, lexicographic sequences, signal analysis, game playing, law enforcement, biometrics, medical diagnosis, equipment maintenance and micro-array data analysis.

The invention pertains to a system and method for dispatching and executing the compute-intensive parts of the workflow for database queries on an attached high-performance, parallel computing platform. The performance overhead for moving the required data and results between the database platform and the high-performance computing platform where the workload is executed is amortized in several ways, for example,

• • by exploiting the fine-grained parallelism and superior hardware performance on the parallel computing platform for speeding up compute-intensive calculations,
  • by using in-memory data structures on the parallel computing platform to cache data sets between a sequence of time-lagged queries on the same data, so that these queries can be processed without further data transfer overheads,
  • by replicating data within the parallel computing platform so that multiple independent queries on the same target data set can be simultaneously processed using independent parallel partitions of the high-performance computing platform.

A specific embodiment of this invention was used for deploying a bio-informatics application involving gene and protein sequence matching using the Smith-Waterman algorithm on a database system connected via an Ethernet local area network to a parallel supercomputer.

Methods for manipulating carbohydrate processing pathways in cells of interest are provided. Methods are directed at manipulating multiple pathways involved with the sialylation reaction by using recombinant DNA technology and substrate feeding approaches to enable the production of sialylated glycoproteins in cells of interest. These carbohydrate engineering efforts encompass the implementation of new carbohydrate bioassays, the examination of a selection of insect cell lines and the use of bioinformatics to identify gene sequences for critical processing enzymes. The compositions comprise cells of interest producing sialylated glycoproteins. The methods and compositions are useful for heterologous expression of glycoproteins.

The present invention is directed to a novel Afx response element comprising the nucleotide sequence AACATGTT, said nucleotide sequence having a DNA binding site for the human fork head transkription factor Afx. The invention also relates to the use of the Afx response element in the screening for genes as diabetes drug targets and in the bioinformatic analysis of the human genome, said genes in turn being useful in other screening methods for compounds modifying the insulin receptor signaling pathway. A further aspect of the invention is a vector construct comprising the novel nucleotide sequence, a host cell transformed with said vector construct as well as the fusion protein expressed by said host cell.

The present invention relates to a novel computer program product to extract and gather peak information from an automated sequencer or bioinformatics tool into a peak database, and to manipulate and analyze the peak information within the database.

A time to event data analysis method and system. The present invention relates to the analysis of data to identify relationships between the input data and one or more conditions. One method of analysing such data is by the use of neural networks which are non-linear statistical data modelling tools, the structure of which may be changed based on information that is passed through the network during a training phase. A known problem that affects neural networks is the issue of overtraining which arises in overcomplex or overspecified systems when the capacity of the network significantly exceeds the needed parameters. The present invention provides a method of analysing data, such as bioinformatics or pathology data, using a neural network with a constrained architecture and providing a continuous output that can be used in various contexts and systems including prediction of time to an event, such as a specified clinical event.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.

Multi-adaptive processing systems and techniques for enhancing parallelism and performance of computational functions are disclosed which can be employed in a myriad of applications including multi-dimensional pipeline computations for seismic applications, search algorithms, information security, chemical and biological applications, filtering and the like as well as for systolic wavefront computations for fluid flow and structures analysis, bioinformatics etc. Some applications may also employ both the multi-dimensional pipeline and systolic wavefront methodologies disclosed.

A gateway enables medical (including genetic and genomic) laboratories and health care providers (collectively “clients”) to communicate electronic messages with each other without developing and maintaining an interface for each peer. The gateway translates messages sent between the parties. The gateway receives messages from each sender in a form, and containing diagnostic codes, preferred by the sender. For each received message, the gateway ascertains an intended receiving client. Each client may specify one or more receivers (such as applications) that are to receive messages sent to the client, as well as a separate form, and optionally a set of codes, for each receiver. For each receiver, the gateway generates translated messages, according to the receiver's preferred form and/or codes. The gateway sends the translated messages to each of the designated receivers. The gateway may include a validation component to check incoming messages to ensure the messages include required information and that information values are valid or acceptable. The gateway may include an exception handler that notifies a sending client if a message from the client fails to be translated or sent correctly. The gateway may maintain a repository in which the gateway stores copies of messages the gateway sent or would have sent to clients. The gateway provides an interface, such as a secure web interface, to this repository. Clients may access messages or lists of messages, especially messages the clients are not otherwise capable of receiving, through this interface. The gateway may store copies of some of the data that flows through the gateway in a bioinformatics database, which may be automatically analyzed by the gateway or queried for research or patient care purposes.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline. Additionally, a second integrated circuit is also provided, where each second integrated circuit forming a field programmable gate array (FPGA), the FPGA being configured by firmware to arrange a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomic processing steps of the sequence analysis pipeline, the set of hardwired digital logic circuits of each FPGA being arranged as a set of processing engines to perform the second set of genomic processing steps. A shared memory is also provided.

The invention provides a virus infection detection and identification technology based on metagenomics. The virus infection detection and identification technology based on the metagenomics comprises the four portions of sample preparation, high-throughput sequencing, bioinformatic analysis and result re-checking. In the sample preparation portion, viral nucleic acid is effectively extracted or enriched from detection samples according to the requirements of the virus infection detection and identification technology based on the metagenomics and characteristics of different types of detection samples, and a nucleic acid library which can be used for a next-generation sequencing instrument is established. In the high-throughput sequencing portion, the nucleic acid library established in the sample preparation step is sequenced so to obtain sufficient high-quality nucleic acid sequence information. In the bioinformatic analysis portion, a large number of high-quality nucleic acid sequences obtained in the high-throughput sequencing step is analyzed to further obtain viral component information prompted by the nucleic acid of the samples. In the result re-checking portion, a bioinformatic analysis result and other information, such as technical contrast, are integrated to perform comprehensive study and judgment, finally alternative infection virus is determined, and other technologies, such as PCR, are utilized to perform re-checking.

Secure network transaction system obtains user-authorized genetic term or bioinformatic profile, and transacts online service according to genetically-based user medical or other risk determined therefrom. Insurance policy, promotional offer, or other service may dynamically address genetically-based condition. Bioinformatic data classifies user per personal mask which filters subset of user genetic sequence. Risk profile may be calculated according to actuarial statistics, genetics and/or heredity using non-discriminatory rules specified for users in temporal or jurisdictional groups. User transactions are modifiable according to bioinformatic data representing genetically-based risk increase or decrease. Data is securely processed, modulated, and stored by network server for remote access and transaction using various portable user devices.

Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including e-Commerce target marketing, bioinformatics (large scale scientific data analysis), and automatic computing (web usage analysis), etc. However, state-of-the-art pattern-based clustering methods (e.g., the pCluster algorithm) can only handle datasets of thousands of records, which makes them inappropriate for many real-life applications. Furthermore, besides the huge data volume, many data sets are also characterized by their sequentiality, for instance, customer purchase records and network event logs are usually modeled as data sequences. Hence, it becomes important to enable pattern-based clustering methods i) to handle large datasets, and ii) to discover pattern similarity embedded in data sequences. There is presented herein a novel method that offers this capability.

Provided herein are devices, systems, and methods of employing the same for the performance of bioinformatics analysis. The apparatuses and methods of the disclosure are directed in part to large scale graphene FET sensors, arrays, and integrated circuits employing the same for analyte measurements. The present GFET sensors, arrays, and integrated circuits may be fabricated using conventional CMOS processing techniques based on improved GFET pixel and array designs that increase measurement sensitivity and accuracy, and at the same time facilitate significantly small pixel sizes and dense GFET sensor based arrays. Improved fabrication techniques employing graphene as a reaction layer provide for rapid data acquisition from small sensors to large and dense arrays of sensors. Such arrays may be employed to detect a presence and/or concentration changes of various analyte types in a wide variety of chemical and/or biological processes, including DNA hybridization and/or sequencing reactions. Accordingly, GFET arrays facilitate DNA sequencing techniques based on monitoring changes in hydrogen ion concentration (pH), changes in other analyte concentration, and/or binding events associated with chemical processes relating to DNA synthesis within a gated reaction chamber of the GFET based sensor.

This invention involves use of temporal or spatio/spector-temporal data (SSTD) for early classification of outputs that are results of spatio-temporal patterns of data. Classification models are based on spiking neural networks (SNN) suitable to learn and classify SSTD. The invention may predict early events in many applications, i.e. engineering, bioinformatics, neuroinformatics, predicting response to treatment of neurological and brain disease, ecology, environment, medicine, and economics, among others. The invention involves a method and system for personalized modelling of SSTD and early prediction of events based on evolving spiking neural network reservoir architecture (eSNNr). The system includes a spike-time encoding module to encode continuous value input information into spike trains, a recurrent 3D SNNr and an eSSN as an output classification module.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline. Additionally, a second integrated circuit is also provided, where each second integrated circuit forming a field programmable gate array (FPGA), the FPGA being configured by firmware to arrange a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomic processing steps of the sequence analysis pipeline, the set of hardwired digital logic circuits of each FPGA being arranged as a set of processing engines to perform the second set of genomic processing steps. A shared memory is also provided.

The invention provides a method for developing a mung bean simple sequence repeat (SSR) primer based on transcriptome sequencing. The method comprises the following steps: obtaining a set of mung bean genome-wide transcription, and forming a sequence database; splicing sequencing sequences into a transcriptome by Trinity; taking the longest transcript in each gene as Unigene; carrying out bioinformatics analysis of a Unigene sequence; carrying out SSR detection on the Unigene by adopting MISA1.0; carrying out SSR primer design by using a Primer 3, and carrying out SSR primer polymorphism identification. 13134 pairs of SSR primers are successfully designed by application of the method; 50 pairs of primers are randomly selected to verify 8 parts of mung bean deoxyribonucleic acids (DNAs) from different countries, wherein 32 pairs of polymorphic primers are formed in all; the mung bean materials with different geographical origins can be distinguished by using the 32 pairs of SSR primers. The method disclosed by the invention is convenient, fast and accurate, and low in cost, and a new thought is provided for development of the mung bean SSR primer.