A molecular data storage system is presented for encoding data-block(s). The system includes one or more populations of molecular sequences, each population encoding a respective one of the data-blocks. Each molecular sequence comprises a data encoding section comprising a sequence of similar predetermined length N of short k-mers, whereby in each population the data encoding sections of all molecular sequences have the similar predetermined length N. The short k-mers serve as data encoding building blocks of the data encoding sections, whereby valid short k-mers serving as data encoding building blocks form a subset of a building-block-set consisting of a number Z of different preselected short k-mers each presenting a unique combination of a number k of bases of a preselected set of bases, characterized in that all the Z types of short k-mers in said building-block-set have a similar predetermined size k≥2 (plurality) of bases. The data encoding sections collectively encode a sequence of encoded alphabet letters S=(π.sup.1, π.sup.2, . . . , π.sup.n . . . , π.sup.N−1, π.sup.N). Each valid encoded alphabet letter π.sup.n at location n of the sequence S of alphabet letters is characterized by occurrence of a predetermined plurality of different types of short k-mers of the building-block-set in a corresponding location n along the data encoding sections of the plurality of molecular sequences of said population.

A molecular data storage system is presented for encoding data-block(s). The system includes one or more populations of molecular sequences, each population encoding a respective one of the data-blocks. Each molecular sequence comprises a data encoding section comprising a sequence of similar predetermined length N of short k-mers, whereby in each population the data encoding sections of all molecular sequences have the similar predetermined length N. The short k-mers serve as data encoding building blocks of the data encoding sections, whereby valid short k-mers serving as data encoding building blocks form a subset of a building-block-set consisting of a number Z of different preselected short k-mers each presenting a unique combination of a number k of bases of a preselected set of bases, characterized in that all the Z types of short k-mers in said building-block-set have a similar predetermined size k≥2 (plurality) of bases. The data encoding sections collectively encode a sequence of encoded alphabet letters S=(π.sup.1, π.sup.2, . . . , π.sup.n . . . , π.sup.N−1, π.sup.N). Each valid encoded alphabet letter π.sup.n at location n of the sequence S of alphabet letters is characterized by occurrence of a predetermined plurality of different types of short k-mers of the building-block-set in a corresponding location n along the data encoding sections of the plurality of molecular sequences of said population.

A computer-implemented method includes: receiving a set of DNA sequences extracted from one or more dust samples collected from a structure; analyzing the sequences using a machine learning estimator, where the machine learning estimator has been trained to distinguish structures with mold growth due to water damage from structures without mold growth due to water damage; and determining if the structure has mold growth due to water damage.

A computer-implemented method includes: receiving a set of DNA sequences extracted from one or more dust samples collected from a structure; analyzing the sequences using a machine learning estimator, where the machine learning estimator has been trained to distinguish structures with mold growth due to water damage from structures without mold growth due to water damage; and determining if the structure has mold growth due to water damage.

Provided are a prediction device and the like capable of more accurately simulating an analysis target. The prediction device generates function information for a gene sequence of a living body to be an analysis target based on first model information representing a relevance between sequence information and the function information, the sequence information representing the gene sequence of the analysis target, the function information representing a function potentially expressed by the gene sequence; and generates prediction information representing observation information predicted for the analysis target based on second model information and the function information, the second model information representing a relevance among the function information of the living body, environment information representing an environment around the living body, and the observation information observed for the living body, the function information being generated for the gene sequence of the analysis target.

Provided are a prediction device and the like capable of more accurately simulating an analysis target. The prediction device generates function information for a gene sequence of a living body to be an analysis target based on first model information representing a relevance between sequence information and the function information, the sequence information representing the gene sequence of the analysis target, the function information representing a function potentially expressed by the gene sequence; and generates prediction information representing observation information predicted for the analysis target based on second model information and the function information, the second model information representing a relevance among the function information of the living body, environment information representing an environment around the living body, and the observation information observed for the living body, the function information being generated for the gene sequence of the analysis target.

A simulation system performs microsimulations to model the impact of one or more early cancer detection screenings for a plurality of participants to simulate a randomized controlled trial (RCT). In one instance, the microsimulations are performed using parallel processing techniques. The microsimulation simulates the impact of early detection screenings on individual trajectories of the participants. In particular, while most screening modalities are for single cancer types, the microsimulation herein simulates the effect of a detection model on individual trajectories for participant populations having multiple types of cancer using, for example, multi-cancer early detection (MCED) screenings that are capable of detecting multiple types of cancer.

A simulation system performs microsimulations to model the impact of one or more early cancer detection screenings for a plurality of participants to simulate a randomized controlled trial (RCT). In one instance, the microsimulations are performed using parallel processing techniques. The microsimulation simulates the impact of early detection screenings on individual trajectories of the participants. In particular, while most screening modalities are for single cancer types, the microsimulation herein simulates the effect of a detection model on individual trajectories for participant populations having multiple types of cancer using, for example, multi-cancer early detection (MCED) screenings that are capable of detecting multiple types of cancer.

A computer-executable application receives genetic test results for a genetic test a patient has undergone, an identifier for the genetic test, and an identifier for the genetic laboratory that performed the genetic test. The application identifies a format type of the genetic test results. When the format type is unstructured, the application performs an optical character recognition process to the genetic test results such that the format type of the genetic test results is semi-structured. When the format type is semi-structured, the application identifies a set of lexing and parsing rules assigned to the genetic test. The application generates processed genetic test results by applying the set of lexing and parsing rules to the genetic test results and stores the processed genetic test results in a data store. When the format type is structured, the application stores the genetic test results as the processed genetic test results in the data store.

A computer-executable application receives genetic test results for a genetic test a patient has undergone, an identifier for the genetic test, and an identifier for the genetic laboratory that performed the genetic test. The application identifies a format type of the genetic test results. When the format type is unstructured, the application performs an optical character recognition process to the genetic test results such that the format type of the genetic test results is semi-structured. When the format type is semi-structured, the application identifies a set of lexing and parsing rules assigned to the genetic test. The application generates processed genetic test results by applying the set of lexing and parsing rules to the genetic test results and stores the processed genetic test results in a data store. When the format type is structured, the application stores the genetic test results as the processed genetic test results in the data store.