A system and method may be provided to receive sample RNA reads from patients and generate lists of genes and their associated RNA expression levels in each patient. Some of the RNA reads may be matched to an RNA transcript or gene or gene family in terms of their match likelihood and other RNA reads may be matched to an RNA transcript or gene or gene family through the use of one or more machine learning classifiers. A machine learning classifier may be trained based on the plurality of the lists and a plurality of corresponding patients’ clinical status data to identify gene patterns that recur with a high degree of frequency in the plurality of the lists. Those gene patterns can be capable of modifying a disease or treatment response and can be targeted for drug/treatment development.

A system and method may be provided to receive sample RNA reads from patients and generate lists of genes and their associated RNA expression levels in each patient. Some of the RNA reads may be matched to an RNA transcript or gene or gene family in terms of their match likelihood and other RNA reads may be matched to an RNA transcript or gene or gene family through the use of one or more machine learning classifiers. A machine learning classifier may be trained based on the plurality of the lists and a plurality of corresponding patients’ clinical status data to identify gene patterns that recur with a high degree of frequency in the plurality of the lists. Those gene patterns can be capable of modifying a disease or treatment response and can be targeted for drug/treatment development.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.

Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.

Described herein are methods of determining segregation dynamics of mitochondrial DNA herein. Also described herein are methods of diagnosing, prognosing, and/or monitoring a mitochondrial disease.

Described herein are methods of determining segregation dynamics of mitochondrial DNA herein. Also described herein are methods of diagnosing, prognosing, and/or monitoring a mitochondrial disease.

Systems and techniques are described to generate amino acid sequences of target proteins based on amino acid sequences of template proteins using machine learning techniques. The amino acid sequences of the target proteins can be generated based on data that constrains the modifications that can be made to the amino acid sequences of the template proteins. In illustrative examples, the template proteins can include antibodies produced by a non-human mammal that bind to an antigen and the target proteins can correspond to human antibodies with a region having at least a threshold amount of identity with the binding region of the template antibody. Generative adversarial networks can be used to produce the amino acid sequences of the target proteins.

Systems and techniques are described to generate amino acid sequences of target proteins based on amino acid sequences of template proteins using machine learning techniques. The amino acid sequences of the target proteins can be generated based on data that constrains the modifications that can be made to the amino acid sequences of the template proteins. In illustrative examples, the template proteins can include antibodies produced by a non-human mammal that bind to an antigen and the target proteins can correspond to human antibodies with a region having at least a threshold amount of identity with the binding region of the template antibody. Generative adversarial networks can be used to produce the amino acid sequences of the target proteins.