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.

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.

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.

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.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

The present disclosure provides a novel integrated entropy-based method that combines genome-wide profiling and network analyses for diagnostic and prognostic applications. The present disclosure further provides the integration of multiomics datasets, network analyses and machine learning that enable predictions on diagnosing infectious diseases and predicting the probability that they will escape treatment/the host immune system and/or become antibiotic resistant. The present disclosure provides a primary gateway towards the development of highly accurate infectious disease prognostics.

Provided herein are methods for accurately determining the alleles present at a locus that is broadly applicable to any locus, including highly polymorphic loci such as HLA loci, BGA loci and HV loci. Embodiments of the disclosed methods are useful in a wide range of applications, including, for example, organ transplantation, personalized medicine, diagnostics, forensics and anthropology.

Provided herein are methods for accurately determining the alleles present at a locus that is broadly applicable to any locus, including highly polymorphic loci such as HLA loci, BGA loci and HV loci. Embodiments of the disclosed methods are useful in a wide range of applications, including, for example, organ transplantation, personalized medicine, diagnostics, forensics and anthropology.

Contemplated systems and methods allow for prediction of chemotherapy outcome for patients diagnosed with high-grade bladder cancer. In particularly preferred aspects, the prediction is performed using a model based on machine learning wherein the model has a minimum predetermined accuracy gain and wherein a thusly identified model provides the identity and weight factors for omics data used in the outcome prediction.