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 proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

The present invention relates to methods of detecting differentially expressed protein expression indicative of COPD in a test sample. The detection of circulating levels of proteins within an identified COPD biomarker signature can aid in COPD diagnosis and disease monitoring, as well as in the prediction of responses to therapeutics. Evaluation of the biomarker signatures disclosed, or a subset of biomarkers thereof, provides a level of discrimination not found with individual markers.

The lack of clear predictors of prostate cancer progression leads to subjective decision-making regarding courses of treatment. The identification of new biomarkers that are predictive of recurrence after radical prostatectomy would advance the field of prostate cancer treatment. Disclosed are miRNAs that can be used as molecular biomarkers to detect or predict the progression of prostate cancer and to adjust a treatment plan accordingly. Furthermore, kits are included for the detection of these miRNAs.

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.

A log of molecular events experienced by a cell and timing indicators for those events are stored in existing polynucleotides through a process of creating a double strand break (“DSB”) in a polynucleotide and inserting a new polynucleotide sequence by repairing the DSB with homology directed repair (“HDR”). The presence, order, and number of new polynucleotide sequences provides a log of events and timing of those events. Cellular mechanisms for creating the DSB and/or repairing with HDR are regulated by intra- or extra-cellular signals. When the log is created in the DNA of a cell, the changes may be heritably passed to subsequent generations of the cell. A correlation between the cellular signals and sequence of inserted HDR templates allows for identification of events and the timing experienced by the cell.

Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. In addition, gene expression data can be used to develop more accurate predictors of survival. The present invention discloses methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention discloses a novel microarray, the Lymph Dx microarray, for obtaining gene expression data from a lymphoma sample. The invention also discloses a variety of methods for utilizing lymphoma gene expression data to determine the identity of a particular lymphoma and to predict survival in a subject diagnosed with a particular lymphoma. This information will be useful in developing the therapeutic approach to be used with a particular subject.

A method for in vitro diagnosing asphyxia and disorders related thereto, a method of in vitro estimating duration of hypoxia in a patient subjected to asphyxia, and a method for in vitro monitoring of normoxic, hypoxic and hyperoxic conditions and/or normobaric and hyperbaric oxygen therapy, includes quantitatively detecting in a biological sample of a patient a plurality of asphyxia specific endogenous compounds which are selected from the group consisting of biogenic amines; carnitine-derived compounds; amino acids; bile acids; carboxylic acids; eicosanoids; lipids; precursors of cholesterol, cholesterol metabolites; prostanoids; and sugars.

This invention provides methods for identifying genes associated with cognitive impairment and for identifying compounds useful in the treatment of cognitive impairment. The methods can in particular be used to identify genes associated with, and compounds useful in treating, cognitive impairment in aging.

The present invention relates to methods for evaluating and/or predicting the outcome of a clinical condition, such as cancer, metastasis, AIDS, autism, Alzheimer's, and/or Parkinson's disorder. The methods can also be used to monitor and track changes in a patient's DNA and/or RNA during and following a clinical treatment regime. The methods may also be used to evaluate protein and/or metabolite levels that correlate with such clinical conditions. The methods are also of use to ascertain the probability outcome for a patient's particular prognosis.