Abstract: A method for generating classifiers for identifying neuropsychiatric disease includes acquiring functional neuroimaging data. The acquired functional neuroimaging data may be registered to an atlas of the brain. A discriminative mask is generated based on the registered functional neuroimaging data and the generated discriminative mask is applied to the registered functional neuroimaging data. One or more classifiers are generated for identifying neuropsychiatric disease based on the masked functional neuroimaging data. The accuracy of the generated classifiers may be verified. The generated classifiers may then be used to identify neuropsychiatric disease.

Abstract: Methods and systems for identifying a primer pair for polymerase chain reaction specific to one or more target genomes. Methods and systems of the present disclosure can be used to identify primers that can distinguish between target genomes and closely related non-target genomes.

Abstract: Methods and systems for identifying a primer pair for polymerase chain reaction specific to one or more target genomes. Methods and systems of the present disclosure can be used to identify primers that can distinguish between target genomes and closely related non-target genomes.

Abstract: A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

Abstract: A system for providing medical decision support for diagnosis and treatment of disease comprises a medical knowledge database comprising medical information, the medical information including probabilities of disease outcomes for a disease of interest, a memory device for storing a program, a processor in communication with the memory device, the processor operative with the program to obtain patient information and in vitro test results for a patient, and automatically generate a recommendation for a medical test based on a combination of the patient information, the in vitro test results, and medical information from the medical knowledge database.

Abstract: A method for generating classifiers for identifying neuropsychiatric disease includes acquiring functional neuroimaging data. The acquired functional neuroimaging data may be registered to an atlas of the brain. A discriminative mask is generated based on the registered functional neuroimaging data and the generated discriminative mask is applied to the registered functional neuroimaging data. One or more classifiers are generated for identifying neuropsychiatric disease based on the masked functional neuroimaging data. The accuracy of the generated classifiers may be verified. The generated classifiers may then be used to identify neuropsychiatric disease.

Abstract: A computer-implemented method for registering objects of interest across a plurality of data acquisition types includes providing image data including the objects of interest corresponding to the plurality of data acquisition types, providing a plurality of constraints on groups which may be determined for the objects of interests determining a set of possible groupings of the objects of interest according to the plurality of constraints, searching the set of possible groupings for groupings of the objects of interest according to an optimization function, and storing the groupings of the objects of interest to a computer-readable media.

Abstract: A system for providing medical decision support for diagnosis and treatment of disease comprises a medical knowledge database comprising medical information, the medical information including probabilities of disease outcomes for a disease of interest, a memory device for storing a program, a processor in communication with the memory device, the processor operative with the program to obtain patient information and in vitro test results for a patient, and automatically generate a recommendation for a medical test based on a combination of the patient information, the in vitro test results, and medical information from the medical knowledge database.

Abstract: A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

Abstract: A system and method for using heterogeneous data from multiple healthcare information sources in a medical decision support system is disclosed. Each healthcare information system stores medical data using a different local schema. The medical decision support system provides responses to user queries. A query is received from a user that is generated in a standardized global schema. The query includes information from medical ontologies. Database queries are generated from the user queries that use the medical ontologies to generate constraints in the queries. The medical ontologies are also used to infer database queries. The generated query is translated into multiple queries for the multiple healthcare systems wherein each query is in the local schema of the healthcare information system that is being queried. Each database query is transmitted to one of the healthcare information systems based on the local schema of the particular query.

Abstract: A method and computer-program product for sequencing nucleic acid sequences using restriction fragment maps derived from end-sequenced nucleotide fragments. The initial nucleotide sequence can be processed to form a shot-gun-data set. The present teachings employ a technique called Restriction Site Shotgun Sequencing (RSSS.) It can reduce the amount of overlap required between fragment ends while still producing a good assembly. A decrease in overlap can be achieved by using additional information in the fragments to assist in determining that two fragments overlap.

Abstract: Many biological data analysis problems require matching data points from different data sets. For example, high-throughput protein expression liquid chromatography and mass spectrometry (LC/MS) protocols developed generate 2-dimensional feature sets that require matching on a point-by-point basis. In these protocols, it can be useful to perform all data collection sequentially and analyze the data subsequent to lab work. This avoids the need to identify all of the components in a sample before comparing it to other samples, thus saving effort and avoiding the potential problem of missing an important component due to problems in the identification stage. Such methods can involve identifying, grouping and measuring sets of characteristic peaks in order to identify and quantify shared peptides. The present teachings provide, among other things, a method for comparing and associating multiple data sets. These methods, can be used, for example, for analyzing LC/MS runs, gel images etc.