Abstract:
The present disclosure is directed to systems and methods for identifying and prioritizing targets, specifically key genetic loci underlying one or more fertility disorders, for non-hormonal female contraceptive drug development.
Abstract:
Embodiments of the present disclosure relate to a machine-learning system for processing medical information. The system comprises a communications interface configured to access electronic medical data. An automated retrieval processor is configured to analyze the electronic medical data to identify and retrieve relevant electronic data based on predefined search criteria. A learning processor is configured to update and optimize the automated retrieval processor based on received electronic metadata associated with the identified relevant electronic data. Other embodiments relate to a machine-learning method for processing electronic medical information. The method comprises accessing electronic medical data from a public database and/or a private database. In addition, the method comprises analyzing the electronic medical data to identify and retrieve relevant electronic data based on predefined search criteria. Also, the method includes performing adaptive learning based on received electronic metadata associated with the identified relevant electronic data.
Abstract:
Embodiments of the present disclosure relate an ovarian characteristic measurement method and system. A data interface is configured to receive a set of reproductive health data of a patient. The reproductive health data is processed using an ovarian characteristic data structure stored in memory. The data structure is generated from an ovarian characteristic data model that defines the ovarian characteristic. For instance, the data model defines a processing of the reproductive health data to yield a measurement of the ovarian characteristic. Accordingly, a format of the ovarian data structure is selected to optimize computing resources required to process the set of reproductive health data. The method and system then determine a value of the ovarian characteristic of the patient based on the processing of the set of reproductive health data.
Abstract:
The present invention generally relates to systems and methods for assessing female fertility and infertility, male fertility and infertility and the combined fertility profile of a male and a female. Systems and methods of the invention determine the fertility potential of a female and a male combined by conducting an assay on a sample obtained from the male and female to determine the presence of one or more fertility-associated genetic variants, obtain fertility-associated phenotypic and/or environmental data from the male and the female, accepting as input data, the genetic variants determined from the female and male and phenotypic and/or environmental exposure data from the male and female, analyze the input data using a prognosis predictor correlated with fertility, and generate a fertility profile that reflects the fertility potential of the male and the female combined by using the prognosis predictor on the input data.
Abstract:
The present invention relates to methods and systems for assessing risk of infertility and ovarian dysfunction and/or diminished ovarian reserve and/or for determining an appropriate course of treatment. In some embodiments, the invention provides methods for assessing likelihood of ovarian dysfunction, including identifying a plurality of genetic variants that are filtered into functional biological pathways. The frequency distribution of the variants in each functional pathway is then compared to frequency distributions obtained from reference sets corresponding to each pathway. Further embodiments of the invention comprise clustering subjects based on patterns in their genetic variants, and identifying phenotypic differences with respect to ovarian dysfunction between clusters of patients.
Abstract:
The present invention relates to methods and systems for assessing ovarian reserve and function in a female subject and informing course of treatment thereof. The invention provides methods for assessing ovarian reserve and function by analyzing both clinical and genetic data/characteristics from a female subject. These methods involve the determination of the presence of one or more mutations in a gene, the gene being associated with fertility and/or ovarian reserve or function. In certain aspects the methods also involve the determination of one or more clinical characteristics associated with fertility and/or ovarian reserve or function. In certain embodiments, the clinical and genetic characteristics obtained from a female subject can be used as data to be input to an ovarian reserve predictor, such that a probability of the female subject suffering from ovarian reserve dysfunction or premature decline can be generated.
Abstract:
Methods for assessing infertility and related pathologies and informing treatment type and timing thereof are provided. According to certain embodiments, methods of the invention include determining levels of one or more transcripts present in a sample obtained from a subject suspected of having endometriosis, identifying transcript levels that correspond to a regulation pattern specific to a time-point in a uterine cycle, and characterizing endometriosis of the subject based upon the identified transcript levels. The invention includes methods for assessing age-associated increase in aneuploidy rates based on FSH levels and IVF success rates based on obesity in PCOS patients.
Abstract:
The invention generally relates to methods for assessing whether a genetic region is associated with infertility comprising producing a genetically modified mouse with an alteration in said region and assessing the mouse for an infertility-associated phenotype.
Abstract:
The invention provides methods for analyzing a patient's potential for achieving ongoing pregnancy with respect to a specific fertility treatment. The methods involve obtaining a sample containing microorganisms from an individual, identifying a number of specific microorganisms present in an individual, and comparing these microorganisms to those known to be associated with reproductive success. The individual is then informed of her or his potential reproductive success based upon the results of the comparison.
Abstract:
Embodiments of the present disclosure relate to a machine-learning system for processing medical information. The system comprises a communications interface configured to access electronic medical data. An automated retrieval processor is configured to analyze the electronic medical data to identify and retrieve relevant electronic data based on predefined search criteria. A learning processor is configured to update and optimize the automated retrieval processor based on received electronic metadata associated with the identified relevant electronic data. Other embodiments relate to a machine-learning method for processing electronic medical information. The method comprises accessing electronic medical data from a public database and/or a private database. In addition, the method comprises analyzing the electronic medical data to identify and retrieve relevant electronic data based on predefined search criteria. Also, the method includes performing adaptive learning based on received electronic metadata associated with the identified relevant electronic data.