The technology described herein is directed to methods of treating and diagnosing bronchial premalignant lesions, e.g. by determining the lesion subtype using one or more biomarkers described herein.

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.

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 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.

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.

Exemplary embodiments relate to a protein engineering pipeline configured to optimize or improve proteins for specified functions. The problem space of such a task can grow quickly based on the sequence of the protein being optimized and the functions for which the protein is being designed. The solutions described herein allow the problem space to be efficiently searched by applying a combination of a protein design pipeline and an evaluation procedure performed on a quantum computer. As a result, single or multiple amino acid substitutions at a site of interest may be predicted in order to generate optimized protein variants.

Exemplary embodiments relate to a protein engineering pipeline configured to optimize or improve proteins for specified functions. The problem space of such a task can grow quickly based on the sequence of the protein being optimized and the functions for which the protein is being designed. The solutions described herein allow the problem space to be efficiently searched by applying a combination of a protein design pipeline and an evaluation procedure performed on a quantum computer. As a result, single or multiple amino acid substitutions at a site of interest may be predicted in order to generate optimized protein variants.