Abstract: Disclosed herein are techniques for implementation of a Hemoglobin A (HBA) assay and an HBA genotyping decision tree matrix in clinical testing. Particularly, aspects are directed to obtaining raw data from the HBA assay performed on a plurality of samples, calculating a first set of probe ratios for each sample based on the raw data, identifying a number of reference samples to be combined as a synthetic reference sample based on the first set of probe ratios, calculating a second set of probe ratios for each sample of the plurality of samples based on the raw data and the synthetic reference sample, and determining, by the decision tree matrix, an HBA genotype for each sample based on the second set of probe ratios for each sample and copy number calling thresholds for sample probe/reference probe ratios associated with each probe of the plurality of probes.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: Embodiments of this invention provide methods, systems, and apparatus for determining whether a fetal chromosomal aneuploidy exists from a biological sample obtained from a pregnant female. Nucleic acid molecules of the biological sample are sequenced, such that a fraction of the genome is sequenced. Respective amounts of a clinically-relevant chromosome and of background chromosomes are determined from results of the sequencing. The determination of the relative amounts may count sequences of only certain length. A parameter derived from these amounts (e.g. a ratio) is compared to one or more cutoff values, thereby determining a classification of whether a fetal chromosomal aneuploidy exists. Prior to sequencing, the biological sample may be enriched for DNA fragments of a particular sizes.

Abstract: A method for making a recombinant gene includes searching a database using a nucleotide sequence of a coding region, a nucleotide sequence that encodes an amino acid sequence, or an amino acid sequence, of a gene, for one or more nucleotide sequences having homology; selecting one or more nucleotide sequences other than nucleotide sequences only derived from a genome from the selected nucleotide sequences; for ones of the selected one or more nucleotide sequences comprising an upstream or downstream nucleotide sequence, analyzing whether the upstream or downstream nucleotide sequence is a functional sequence to select one or more first functional sequences; for ones of the selected one or more nucleotide sequences comprising no upstream or downstream nucleotide sequence, analyzing whether a gene information has any description indicating a functional sequence to select one or more second functional sequences; scoring the selected functional sequences; and selecting one or more functional sequences.

Abstract: The present invention relates to tissue and cell imaging utilizing genomic informatics and gene-expression profiling. Gene-expression profiles utilized in methods to obtain in situ imaging of cells and tissues provide complex molecular fingerprints regarding the relative state of a cell or tissue.

Abstract: The invention relates to a novel method for the diagnosis of non-alcoholic steatohepatitis (NASH), and for classifying a subject as a potential receiver of a treatment for NASH.

Abstract: A machine-learned model can be trained on and applied to oligonucleotide data. The machine-learned model can be, for example, a neural network, a random forest classifier, or a regression model, and can be trained in one or more stages. The machine-learned model can be applied in design settings, for instance by being configured to predict biophysical effects corresponding to oligonucleotides, by processing real-world experimental or laboratory data, and by retraining the machine-learned model in response to the processed data.

Abstract: Embodiments of this invention provide methods, systems, and apparatus for determining whether a fetal chromosomal aneuploidy exists from a biological sample obtained from a pregnant female. Nucleic acid molecules of the biological sample are sequenced, such that a fraction of the genome is sequenced. Respective amounts of a clinically-relevant chromosome and of background chromosomes are determined from results of the sequencing. A parameter derived from these amounts (e.g. a ratio) is compared to one or more cutoff values, thereby determining a classification of whether a fetal chromosomal aneuploidy exists.

Abstract: Embodiments of this invention provide methods, systems, and apparatus for determining whether a fetal chromosomal aneuploidy exists from a biological sample obtained from a pregnant female. Nucleic acid molecules of the biological sample are sequenced, such that a fraction of the genome is sequenced. Respective amounts of a clinically-relevant chromosome and of background chromosomes are determined from results of the sequencing. The determination of the relative amounts may count sequences of only certain length. A parameter derived from these amounts (e.g. a ratio) is compared to one or more cutoff values, thereby determining a classification of whether a fetal chromosomal aneuploidy exists. Prior to sequencing, the biological sample may be enriched for DNA fragments of a particular sizes.

Abstract: Methods described herein include receiving data from flowing a plurality of aptamers over a sample of tumor cells randomly affixed to a surface of a microfluidic device. The tumor cells may include one or more unknown tumor subtypes of cells. The plurality of aptamers may include a plurality of aptamer families. Each aptamer family of the plurality of aptamer families may be determined to bind to at least one possible subtype of the tumor cells. The data may include a measure of binding affinity of each aptamer family to the tumor cells. The method may include analyzing the measure of the binding affinity of each aptamer family to the tumor cells. The analyzing may include classifying the binding affinity. The method may also include determining one or more aptamer families that characterize the one or more unknown tumor subtypes of cells based on the classifying.