20240029827. METHOD FOR DETERMINING THE PATHOGENICITY/BENIGNITY OF A GENOMIC VARIANT IN CONNECTION WITH A GIVEN DISEASE simplified abstract (Engenome S.R.L.)

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METHOD FOR DETERMINING THE PATHOGENICITY/BENIGNITY OF A GENOMIC VARIANT IN CONNECTION WITH A GIVEN DISEASE

Organization Name

Engenome S.R.L.

Inventor(s)

Ivan Limongelli of Pavia (IT)

Giovanna Nicora of Pavia (IT)

METHOD FOR DETERMINING THE PATHOGENICITY/BENIGNITY OF A GENOMIC VARIANT IN CONNECTION WITH A GIVEN DISEASE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240029827 titled 'METHOD FOR DETERMINING THE PATHOGENICITY/BENIGNITY OF A GENOMIC VARIANT IN CONNECTION WITH A GIVEN DISEASE

Simplified Explanation

The abstract describes a method for determining the pathogenicity or benignity of a genomic variant in connection with a given disease. The method involves accessing genomic data of a patient's variants and verifying whether each variant meets predefined pathogenicity/benignity criteria. The input information, including the criteria and evidence level, is prepared for a trained algorithm using artificial intelligence and/or machine learning. The algorithm processes the input information to obtain an output representative of the pathogenicity/benignity of each variant.

  • The method involves accessing genomic data of a patient's variants.
  • Each variant is checked against predefined pathogenicity/benignity criteria.
  • The criteria can be related to known conditions or patient-specific conditions.
  • Input information, including criteria and evidence level, is prepared for a trained algorithm.
  • The algorithm processes the input information to determine the pathogenicity/benignity of each variant.

Potential applications of this technology:

  • Genetic testing and diagnosis: This method can be used in genetic testing to determine the pathogenicity or benignity of genomic variants associated with a given disease. It can aid in the diagnosis of genetic disorders.
  • Precision medicine: The method can assist in personalized medicine by providing information about the pathogenicity or benignity of specific genomic variants in individual patients. This can help tailor treatment plans and interventions.

Problems solved by this technology:

  • Variant interpretation: The method addresses the challenge of interpreting the pathogenicity or benignity of genomic variants, which is crucial for accurate diagnosis and treatment decisions.
  • Efficiency and accuracy: By utilizing artificial intelligence and machine learning, the method can process large amounts of genomic data and apply predefined criteria to determine the pathogenicity or benignity of variants. This improves efficiency and accuracy compared to manual interpretation.

Benefits of this technology:

  • Improved diagnostic accuracy: The method can provide more accurate information about the pathogenicity or benignity of genomic variants, leading to improved diagnostic accuracy in genetic testing.
  • Personalized treatment: By considering patient-specific conditions, the method enables personalized treatment plans based on the pathogenicity or benignity of specific variants.
  • Time and cost savings: The use of artificial intelligence and machine learning algorithms can streamline the variant interpretation process, saving time and reducing costs associated with manual interpretation.


Original Abstract Submitted

a method is for determining the pathogenicity/benignity of a genomic variant in connection with a given disease includes accessing genomic data in a list of the patient's genomic variants and for each variant detected, verifying whether or not the variant meets each predefined pathogenicity/benignity criteria. each of such pathogenicity/benignity criterion is a proposition, which can be true or false, related to the variant for a previously known condition or a patient-specific condition. input information is prepared for a trained algorithm using artificial intelligence and/or machine learning. the input information includes information related to the pathogenicity/benignity criteria associated with the level of evidence met by the variant. the input information is processed by the trained algorithm, to obtain an output information representative of the pathogenicity/benignity of each variant. the algorithm is trained in a preliminary step of training.

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