A meter value that reflects the amount of genetic material stored in a reserve is stored in a database for each reserve in a bank. Meter values allow a user to track the amount of genetic material in the reserves of a bank without needing to physically measure or disturb the reserves unnecessarily. As users withdraw and deposit genetic material from and into a reserve, the meter value is changed to reflect the change in the amount of genetic material in the reserve. In certain embodiments, the use of meter values enables accurate and instant accounting of a large number of reserves of genetic material for a large number of individuals. Users and/or individuals may be notified when a meter value falls below a threshold. Notifications may prompt a user to generate additional genetic material from biological sample or an individual to provide additional biological sample.

A meter value that reflects the amount of genetic material stored in a reserve is stored in a database for each reserve in a bank. Meter values allow a user to track the amount of genetic material in the reserves of a bank without needing to physically measure or disturb the reserves unnecessarily. As users withdraw and deposit genetic material from and into a reserve, the meter value is changed to reflect the change in the amount of genetic material in the reserve. In certain embodiments, the use of meter values enables accurate and instant accounting of a large number of reserves of genetic material for a large number of individuals. Users and/or individuals may be notified when a meter value falls below a threshold. Notifications may prompt a user to generate additional genetic material from biological sample or an individual to provide additional biological sample.

The invention describes the method allowing for efficient predictive ranking of clinical efficiencies of the existing targeted medicinal products for individual patient with proliferative or oncology disease. The method makes it possible to use a wide range of experimental data received from the patients' pathological tissue samples and relevant control samples: information on gene mutations, transcription factor binding profile, protein (considering harmonization), mRNA (considering harmonization) and microRNA expression strength. The method also uses information on molecular targets of the medicinal products. This method can be automated to prevent potential errors associated with manual calculation and makes it possible to consider patient-specific changes in hundreds and thousands molecular pathways which include tens and hundreds of gene products. This method also considers the features and mode of action of various classes of target drugs. Using this method will enable selecting a medicinal product for the patient based on the analysis of objective individual changes occurred in the pathological tissue.

The invention describes the method allowing for efficient predictive ranking of clinical efficiencies of the existing targeted medicinal products for individual patient with proliferative or oncology disease. The method makes it possible to use a wide range of experimental data received from the patients' pathological tissue samples and relevant control samples: information on gene mutations, transcription factor binding profile, protein (considering harmonization), mRNA (considering harmonization) and microRNA expression strength. The method also uses information on molecular targets of the medicinal products. This method can be automated to prevent potential errors associated with manual calculation and makes it possible to consider patient-specific changes in hundreds and thousands molecular pathways which include tens and hundreds of gene products. This method also considers the features and mode of action of various classes of target drugs. Using this method will enable selecting a medicinal product for the patient based on the analysis of objective individual changes occurred in the pathological tissue.

A relevance searching method performed by a computer, the relevance searching method includes generating a combined database by combining a plurality of databases each including a plurality of elements and relevance information indicating direct relevance between two elements in the plurality of elements; and searching for relevance between two elements that do not have direct relevance by using the combined database.

A relevance searching method performed by a computer, the relevance searching method includes generating a combined database by combining a plurality of databases each including a plurality of elements and relevance information indicating direct relevance between two elements in the plurality of elements; and searching for relevance between two elements that do not have direct relevance by using the combined database.

A method for verifying a material data chain (MDC) that is maintained by a creator is disclosed. The method includes receiving an unverified portion of the MDC from the creator including a set of consecutive material data blocks (MDBs). Each respective MDB includes respective material data, respective metadata, and a creator verification value. The method includes modifying a genomic differentiation object assigned to the verification cohort based on first genomic regulation instructions (GRI) that were used by the creator to generate the creator verification value. For each MDB in the unverified portion, the method includes determining a verifier verification value based on the MDB, a preceding MDB in the MDC, and a genomic engagement factor (GEF) determined with respect to the MDB. The GEF corresponding to an MDB is determined by extracting a sequence from the metadata of a MDB and mapping the sequence into the modified genomic differentiation object.

A method for verifying a material data chain (MDC) that is maintained by a creator is disclosed. The method includes receiving an unverified portion of the MDC from the creator including a set of consecutive material data blocks (MDBs). Each respective MDB includes respective material data, respective metadata, and a creator verification value. The method includes modifying a genomic differentiation object assigned to the verification cohort based on first genomic regulation instructions (GRI) that were used by the creator to generate the creator verification value. For each MDB in the unverified portion, the method includes determining a verifier verification value based on the MDB, a preceding MDB in the MDC, and a genomic engagement factor (GEF) determined with respect to the MDB. The GEF corresponding to an MDB is determined by extracting a sequence from the metadata of a MDB and mapping the sequence into the modified genomic differentiation object.

A device for the storage and/or the editing of digital data including at least one double stranded, replicative, composite nucleic acid molecule. The composite nucleic acid molecule includes both digital data-encoding and non-digital data-encoding nucleic acids. The non-digital data-encoding nucleic acids may allow indexing and/or the provision of metadata for the flanking digital data-encoding nucleic acid. The composite nucleic acid molecules may be pooled to constitute an array and arrays may constitute a DNA drive, which represents the physical support on which the digital data are stored.

A device for the storage and/or the editing of digital data including at least one double stranded, replicative, composite nucleic acid molecule. The composite nucleic acid molecule includes both digital data-encoding and non-digital data-encoding nucleic acids. The non-digital data-encoding nucleic acids may allow indexing and/or the provision of metadata for the flanking digital data-encoding nucleic acid. The composite nucleic acid molecules may be pooled to constitute an array and arrays may constitute a DNA drive, which represents the physical support on which the digital data are stored.