A system for updating a descriptor trail using artificial intelligence. The system is configured to display on a graphical user interface operating on a processor connected to a memory an element of diagnostic data. The system is configured to receive from a user client device an element of user constitutional data. The system is configured to display on a graphical user interface the element of user constitutional data. The system is configured to prompt an advisor input on a graphical user interface. The system is configured to receive from an advisor client device an advisor input containing an element of advisory data. The system is configured to generate an updated descriptor trail as a function of the advisor input. The system is configured to display the updated descriptor trail on a graphical user interface.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

Disclosed herein is a system for predicting a loss of the function of genetic variants. The system includes a loss of function (LoF) prediction unit for calculating a probability that a target genetic variant will cause a loss of function (LoF) in a target gene through logistic regression with respect to a first probability that the target gene will be intolerant of the loss of function and a second probability that the target genetic variant contained in the target gene will be intolerant.

Disclosed herein is a system for predicting a loss of the function of genetic variants. The system includes a loss of function (LoF) prediction unit for calculating a probability that a target genetic variant will cause a loss of function (LoF) in a target gene through logistic regression with respect to a first probability that the target gene will be intolerant of the loss of function and a second probability that the target genetic variant contained in the target gene will be intolerant.

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.

Systems and methods for retrieval of information from read-only databases that hold taxonomic-related and sequence-related data. A method may include receiving organism names from a taxonomy database and detecting new organism names. The method may also include retrieving hierarchical data and assigning the new organism names to buckets based on the hierarchical data. The method may further include receiving sequence data elements from a nucleotide database, identifying particular buckets to correspond to a screener data set, querying organism names assigned to the particular buckets with names of reference sequences of the sequence data elements, generating a mapping between the sequence data elements and organism names returned as a result of the queries, and storing the mapping.

Systems and methods for retrieval of information from read-only databases that hold taxonomic-related and sequence-related data. A method may include receiving organism names from a taxonomy database and detecting new organism names. The method may also include retrieving hierarchical data and assigning the new organism names to buckets based on the hierarchical data. The method may further include receiving sequence data elements from a nucleotide database, identifying particular buckets to correspond to a screener data set, querying organism names assigned to the particular buckets with names of reference sequences of the sequence data elements, generating a mapping between the sequence data elements and organism names returned as a result of the queries, and storing the mapping.