A system (100) for classifying a biological test sample, including a database (112) populated with reference expression data. The reference expression data includes expression levels of a plurality of molecules (polynucleotides or polypeptides), including a set of marker molecules, in a plurality of reference samples. Each reference sample has a pre-assigned value for each of one or more clinically significant variables. The system includes at least one processor (110) and at least one storage medium containing program instructions for execution by said processor (110). The program instructions cause the processor to accept (122) input expression data including a test vector of expression levels of the marker molecules in the biological test sample; and pass the input expression data to one or more analysis programs (130a, 130b, 135). The analysis programs include at least one statistical classification program (135) for assigning a value of at least one of said clinically significant variables to the test sample.

A computer-implemented method for providing a personalized evaluation of assessment of atherosclerotic plaques for a patient acquiring patient data comprising non-invasive patient data, medical images of the patient, and blood biomarkers. Features of interest are extracted from the patient data and one or more machine learning models are applied to the features of interest to predict one or more measures of interest related to atherosclerotic plaque.

The disclosed embodiments concern index oligonucleotides configured to identify sources of samples of nucleic acids and methods, apparatus, systems and computer program products for identifying and making the index oligonucleotides. In some implementations, the index oligonucleotides include a set of index sequences, a Hamming distance between any two index sequences of the set of index sequences meeting one or more criteria. System, apparatus, and computer program products are also provided for determining a sequence of interest using the index oligonucleotides.

Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.

The present disclosure provides a method of classifying a glioma in a patient by identifying with respect to the glioma, isocitrate dehydrogenase genes (IDH) mutation status, DNA methylation cluster, RNA cluster, telomere length, telomere maintenance, and at least one biomarker, and based in the identifications, classifying the glioma as IDH mutant/G-CIMP low glioma type, IDH mutant/G-CIMP high glioma type, IDH mutant/Codel glioma type, DH wild type/Classic like glioma type, IDH wild type/Mesenchymal-like glioma type, IDH wild type/LGm6-GBM glioma type, or PA-like glioma type.

The components and structure for a genome created for the purpose of the evolutionary development of artificial intelligence systems/machines without human intervention.

The invention provides microarray systems and methods for pathogen identification and characterization. Aspects of the invention implement supervised learning for microarray data analysis to enhance the accuracy and scope of genomic and diagnostic information obtained. Embodiments of the invention, for example, utilize structured logical combinations of the output of independent supervised learning algorithms, such as artificial neural network (ANN) algorithms, to provide an efficient and rapid pathway to clinically and epidemiologically relevant diagnostic information.

The invention provides methods to identify pRNA- and pDNA oligomer affinity pairs. Affinity pairs comprised of nucleic acid oligomers which demonstrate no cross-reactivity (orthogonal) are designed using software and empirically verified by thermodynamic study and lateral flow testing. The design software uses a semi-random algorithm to build such sequences of nucleic acid oligomers based on user-input parameters for affinity strength and orthogonal stringency. These pairs can be applied for use in multi-analyte solid support and lateral flow diagnostic tests.

Provided herein are devices and methods suitable for sequencing, detecting, amplifying, analyzing, and performing sample preparation procedures for nucleic acids and other molecules. In some cases, the devices and methods provided herein are used for computation.