Abstract: The present disclosure provides systems and methods for machine learning classification and assessment of disease based on gene expression data. In an aspect, a method for determining a disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of disease-associated genomic loci; (b) computer processing the data set to determine the disease state of the subject; and (c) electronically outputting a report indicative of the disease state of the subject. In some embodiments, the plurality of disease-associated genomic loci comprises single nucleotide polymorphisms (SNPs). In some embodiments, the disease comprises a lupus condition. In some embodiments, the disease comprises cardiovascular disease (CVD).

Abstract: The invention provides systems and methods for diagnosing and treating an inflammatory skin disease in a patient. The described methods may be used for longitudinal monitoring of the development, progression, or regression of a skin disease including lupus, psoriasis, atopic dermatitis, and systemic sclerosis (scleroderma) condition in a patient, in response to a therapy administered for the condition.

Abstract: The present disclosure provides systems and methods for machine learning classification and assessment of disease based on gene expression data. In an aspect, a method for determining a disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of disease-associated genomic loci; (b) computer processing the data set to determine the disease state of the subject; and (c) electronically outputting a report indicative of the disease state of the subject. In some embodiments, the plurality of disease-associated genomic loci comprises single nucleotide polymorphisms (SNPs). In some embodiments, the disease comprises a lupus condition. In some embodiments, the disease comprises cardiovascular disease (CVD).

Abstract: A method for assessing a lupus nephritis disease state of a patient, the method comprising: analyzing a data set comprising or derived from gene expression measurement data of at least 2 genes or human orthologs thereof selected from the genes listed in Tables 19-1 to 19-36, Tables 19A-1 to 19A-36, Table 20, Table 21, Table 22, Tables 23-1 to 23-28, Tables 25-1 to 25-32, Tables 26-1 to 26-60, Tables 27-1 to 27-48, and Tables 28-1 to 28-22 in a biological sample from the patient, to classify the lupus nephritis disease state of the patient.

Abstract: Methods and systems for diagnosis and treatment of lupus in a patient is disclosed. The method can include analyzing a data set comprising or derived from gene expression measurements of at least 2 genes selected from the genes listed in each of one or more Tables selected from Tables: 1 to 11 to determine a set of genes enriched in a biological sample obtained or derived from the patient, and diagnosing lupus in the patient based on enrichment of the set of genes, wherein the gene expression measurements are obtained from the biological sample.

Abstract: The present disclosure provides methods for determining biological pathways and nucleotide polymorphisms (NPs) shared between two diseases.

Abstract: The present disclosure provides a method for assessing a lupus state of a patient, the method comprising: analyzing a data set comprising and/or derived from gene expression measurements of at least 2 genes selected from genes listed in Tables: 1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; and 32, to classify the lupus state of the patient, wherein the gene expression measurements are obtained from a biological sample obtained or derived from the patient.

Abstract: The present disclosure provides systems and methods for classifying lupus disease state of a patient is disclosed. The method can include analyzing a patient data set comprising or derived from gene expression measurements data of at least 2 genes, from a biological sample obtained or derived from the patient, to classify the lupus disease state of the patient. The at least 2 genes can be selected from Tables 17-1 to 17-30, and/or Tables 24-1 to 24-30.

Abstract: The present disclosure provides systems and methods for classifying lupus disease state of a patient is disclosed. The method can include analyzing a patient data set comprising or derived from gene expression measurements data of at least 2 genes, from a biological sample obtained or derived from the patient, to classify the lupus disease state of the patient. The at least 2 genes can be selected from Tables 17-1 to 17-30, and/or Tables 24-1 to 24-30.

Abstract: The present disclosure provides systems and methods for machine learning classification and assessment of COVID-19 disease based on gene expression data, including prediction of disease severity. In an aspect, a method for determining a COVID-19 disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample of each of a plurality of COVID-19 disease-associated genes; (b) computer processing the data set to determine the COVID-19 disease state of the subject; and (c) electronically outputting a report indicative of the COVID-19 disease state of the subject.

Abstract: Disclosed are computer-implemented methods, systems, and media for analyzing patient-reported outcome (PRO) data. A method for determining a method of evaluation and treatment for patients may include receiving PRO data; validating the PRO data; inputting the PRO data to a first machine learning model; generating, using the first machine learning model, based on the PRO data. i) a score indicative of at least one of an activity of the patient's disease, an effectiveness of current treatment, or severity of the patient's reactions, and/or ii) an inference indicative of the disease state of the patient; and generating, recommending, and/or selecting based on the score and/or the inference, an action item, a second method of evaluation, and/or a second method of treatment for the patient.

Abstract: The present disclosure provides systems and methods for classifying lupus disease state of a patient is disclosed. The method can include analyzing a patient data set comprising or derived from gene expression measurements data of at least 2 genes, from a biological sample obtained or derived from the patient, to classify the lupus disease state of the patient. The at least 2 genes can be selected from Tables 17-1 to 17-30, and/or Tables 24-1 to 24-30.

Abstract: Described are machine learning methods of identifying one or more records having a specific phenotype to enable proper correlation between genetic records and phenotypes. In an aspect, a method of identifying one or more records having a specific phenotype may comprise: (a) receiving a plurality of first records, each associated with one or more of a plurality of phenotypes; (b) receiving a plurality of second records, each associated with one or more of the phenotypes, wherein the first and second records are non-overlapping; (c) applying a machine learning algorithm to at least one first record and at least one second record to determine a classifier; (d) receiving a plurality of third records, distinct from the first and second records; and (e) applying the classifier to the third records to identify one or more third records associated with the specific phenotype.

Abstract: The present disclosure provides systems and methods for machine learning classification and assessment of disease based on gene expression data. In an aspect, a method for determining a disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of disease-associated genomic loci; (b) computer processing the data set to determine the disease state of the subject; and (c) electronically outputting a report indicative of the disease state of the subject. In some embodiments, the plurality of disease-associated genomic loci comprises single nucleotide polymorphisms (SNPs). In some embodiments, the disease comprises a lupus condition. In some embodiments, the disease comprises cardiovascular disease (CVD).

Abstract: The present disclosure provides methods for assessing a skin of a subject based on gene expression analysis. In an aspect, a method for assessing a skin of a subject comprises: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample from each of a plurality of inflammatory skin disease-associated genomic loci, e.g.

Abstract: The present disclosure provides systems and methods for machine learning classification of lung nodules based on gene expression data and clinical characteristics data. The method can include, a) obtaining a data set containing gene expression measurements of a biological sample from a patient of at least two lung disease-associated genes, and clinical characteristics data of one or more clinical characteristics of the patient; b) providing the data set as an input to a machine-learning model trained to generate an inference of whether the data set is indicative of a malignant lung nodule or a benign lung nodule; c) receiving, as an output of the machine-learning model, the inference indicating whether the data set is indicative of the malignant lung nodule or the benign lung nodule; and d) electronically outputting a report classifying the lung nodule of the patient as the malignant lung nodule or the benign lung nodule.

Abstract: The present disclosure provides systems and methods for machine learning classification and assessment of COVID-19 disease based on gene expression data. In an aspect, a method for determining a COVID-19 disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of COVID-19 disease-associated genomic loci; (b) computer processing the data set to determine the COVID-19 disease state of the subject; and (c) electronically outputting a report indicative of the COVID-19 disease state of the subject.

Abstract: Described are machine learning methods of identifying one or more records having a specific phenotype to enable proper correlation between genetic records and phenotypes. In an aspect, a method of identifying one or more records having a specific phenotype may comprise: (a) receiving a plurality of first records, each associated with one or more of a plurality of phenotypes; (b) receiving a plurality of second records, each associated with one or more of the phenotypes, wherein the first and second records are non-overlapping; (c) applying a machine learning algorithm to at least one first record and at least one second record to determine a classifier; (d) receiving a plurality of third records, distinct from the first and second records; and (e) applying the classifier to the third records to identify one or more third records associated with the specific phenotype.

Abstract: The invention relates to the field of medicine. The invention provides methods for generating glatiramer-acetate-specific human T-cell lines, and assays that use these T-cell lines for demonstrating immunological identity between glatiramer acetate preparations. These assays allow sensitive and accurate comparison of glatiramer acetate preparations, and find utility as lot-release assays.

Abstract: Disclosed are computer-implemented methods, systems, and media for clustering cells using gene differential expression of single cells. In an aspect, a method may comprise: mapping RNA-Seq data of a plurality of cells onto a sphere (e.g., a hypersphere); calculating a plurality of distances, each of which is associated with an angle between two different cells mapped onto the sphere; clustering the plurality of cells into two clusters based on the plurality of distances; evaluating each of the two clusters using a pre-determined stopping criterion; and repeating the clustering and evaluating on each of the two clusters until the pre-determined stopping criterion or a second stopping criterion is met.