A system for producing a therapeutic oligomer includes a computing device configured to design a proposed therapeutic oligomer sequence, wherein designing further comprises generating a genomic library for an organism from a gene target, initiating a sequence identification function, identifying a genomic locus that the proposed therapeutic oligomer sequence is predicted to bond to as a function of an off-target sequence function, selecting the proposed therapeutic oligomer sequence as a function of the sequence identification function, the genomic locus, and a criterion element, and synthesize a therapeutic oligomer as a function of the proposed therapeutic oligomer sequence.

Provided herein are methods for detecting cells in a subject that express aberrant proteins. Methods are also provided for eliminating such cells expressing aberrant proteins.

The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the updated feature set.

The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the updated feature set.

Methods are disclosed for determining the minimal residual cancer status of an individual utilizing assays that detect cancer associated genetic variation in extracellular DNA. The disclosed methods provide for personalized cancer detection based on the genetic profile of solid cancer tissue of an individual under study. The disclosed methods further provide for noise reduction in the sequencing of extracellular DNA and reduced false positive rates in minimal residual cancer status determination.

Methods are disclosed for determining the minimal residual cancer status of an individual utilizing assays that detect cancer associated genetic variation in extracellular DNA. The disclosed methods provide for personalized cancer detection based on the genetic profile of solid cancer tissue of an individual under study. The disclosed methods further provide for noise reduction in the sequencing of extracellular DNA and reduced false positive rates in minimal residual cancer status determination.

Methods, apparatuses, devices and systems for genetic testing and model training are provided. A genetic testing method includes: obtaining genetic data to be processed, an average number of genetic segments corresponding to each position in the genetic data to be processed being less than or equal to a preset threshold; inputting the genetic data to be processed into a feature generation network layer for performing a feature extraction operation to obtain genetic features corresponding to the genetic data to be processed and enhanced features corresponding to the genetic features; and inputting the genetic data to be processed and the enhanced features into a genetic identification network layer for performing a genetic testing operation to obtain a testing result. The present disclosure realizes performing feature extraction operations through low-depth genetic data, obtaining genetic features and enhanced features corresponding to the genetic features, and performing testing operations based on the enhanced features.

Methods, apparatuses, devices and systems for genetic testing and model training are provided. A genetic testing method includes: obtaining genetic data to be processed, an average number of genetic segments corresponding to each position in the genetic data to be processed being less than or equal to a preset threshold; inputting the genetic data to be processed into a feature generation network layer for performing a feature extraction operation to obtain genetic features corresponding to the genetic data to be processed and enhanced features corresponding to the genetic features; and inputting the genetic data to be processed and the enhanced features into a genetic identification network layer for performing a genetic testing operation to obtain a testing result. The present disclosure realizes performing feature extraction operations through low-depth genetic data, obtaining genetic features and enhanced features corresponding to the genetic features, and performing testing operations based on the enhanced features.

Systems and methods for determining infectious disease states are provided. An ensemble classifier is obtained using a training dataset including labels and attribute values for a plurality of genes including at least 20 genes selected from one or more of Table 1, Table 2, Table 8, and Table 9. For each of a plurality of random seeds, initial classifiers with pseudo-randomly assigned hyperparameters are binned and downsampled using evaluation scores obtained from one or more iterations of K-fold cross-validation. The ensemble classifier is formed from initial classifiers with the best score for each random seed. Infectious disease states are determined for a test subject by inputting attribute values for the plurality of genes to a trained ensemble classifier. Compositions and kits for determining infectious disease states, including amplification primers for the plurality of genes, are further provided.

Systems and methods for determining infectious disease states are provided. An ensemble classifier is obtained using a training dataset including labels and attribute values for a plurality of genes including at least 20 genes selected from one or more of Table 1, Table 2, Table 8, and Table 9. For each of a plurality of random seeds, initial classifiers with pseudo-randomly assigned hyperparameters are binned and downsampled using evaluation scores obtained from one or more iterations of K-fold cross-validation. The ensemble classifier is formed from initial classifiers with the best score for each random seed. Infectious disease states are determined for a test subject by inputting attribute values for the plurality of genes to a trained ensemble classifier. Compositions and kits for determining infectious disease states, including amplification primers for the plurality of genes, are further provided.