The present invention teaches multiple three-dimensional nanosensing geometries for simultaneously assaying both large and small bio-related molecules in one device. The invention delivers broader sensitivity and selectivity than devices that assay small or large molecules separately. The combination assays all classes of molecules, e.g., proteins, lipoproteins, nucleoproteins, lipids, phospholipids, carbohydrates, nucleic acids, simple sugars, hormones, volatile organic compounds, drugs, drug metabolites, etc. Broad collection enables i) rapid and accurate diagnosis, ii) likely courses of treatments, and iii) timely feedback that monitors and follows the progressions of treatment(s). In one example, a patient's pattern of blood lipids, proteins—including proteins with alternate cleavage patterns, peptides—including endocrine peptides, thyroxine (and/or other hormones), and drug metabolites, forms a profile specific to that patient at that time. The profile is inputted for analysis by comparing it to a library of pooled data. Applying artificial intelligence (AI) to this comparison allows accurate diagnosis and then can suggest historically validated treatments most suited to that patient.

The present invention teaches multiple three-dimensional nanosensing geometries for simultaneously assaying both large and small bio-related molecules in one device. The invention delivers broader sensitivity and selectivity than devices that assay small or large molecules separately. The combination assays all classes of molecules, e.g., proteins, lipoproteins, nucleoproteins, lipids, phospholipids, carbohydrates, nucleic acids, simple sugars, hormones, volatile organic compounds, drugs, drug metabolites, etc. Broad collection enables i) rapid and accurate diagnosis, ii) likely courses of treatments, and iii) timely feedback that monitors and follows the progressions of treatment(s). In one example, a patient's pattern of blood lipids, proteins—including proteins with alternate cleavage patterns, peptides—including endocrine peptides, thyroxine (and/or other hormones), and drug metabolites, forms a profile specific to that patient at that time. The profile is inputted for analysis by comparing it to a library of pooled data. Applying artificial intelligence (AI) to this comparison allows accurate diagnosis and then can suggest historically validated treatments most suited to that patient.

Techniques for identifying missing evidence are provided. A plurality of documents, each comprising digitally encoded natural language text data, is received. The plurality of documents is processed to determine a plurality of pair-wise comparisons between a plurality of therapies, where each of the plurality of pair-wise comparisons indicate a relative efficacy of at least one therapy in the plurality of therapies, as compared to at least one other therapy in the plurality of therapies. A knowledge graph is generated based at least in part on aggregating the plurality of pair-wise comparisons, and the knowledge graph is analyzed to identify one or more knowledge gaps within the knowledge graph. Finally, at least an indication of the identified one or more knowledge gaps is output.

Techniques for identifying missing evidence are provided. A plurality of documents, each comprising digitally encoded natural language text data, is received. The plurality of documents is processed to determine a plurality of pair-wise comparisons between a plurality of therapies, where each of the plurality of pair-wise comparisons indicate a relative efficacy of at least one therapy in the plurality of therapies, as compared to at least one other therapy in the plurality of therapies. A knowledge graph is generated based at least in part on aggregating the plurality of pair-wise comparisons, and the knowledge graph is analyzed to identify one or more knowledge gaps within the knowledge graph. Finally, at least an indication of the identified one or more knowledge gaps is output.

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

The technology disclosed directly operates on sequencing data and derives its own feature filters. It processes a plurality of aligned reads that span a target base position. It combines elegant encoding of the reads with a lightweight analysis to produce good recall and precision using lightweight hardware. For instance, one million training examples of target base variant sites with 50 to 100 reads each can be trained on a single GPU card in less than 10 hours with good recall and precision. A single GPU card is desirable because it a computer with a single GPU is inexpensive, almost universally within reach for users looking at genetic data. It is readily available on could-based platforms.

The technology disclosed directly operates on sequencing data and derives its own feature filters. It processes a plurality of aligned reads that span a target base position. It combines elegant encoding of the reads with a lightweight analysis to produce good recall and precision using lightweight hardware. For instance, one million training examples of target base variant sites with 50 to 100 reads each can be trained on a single GPU card in less than 10 hours with good recall and precision. A single GPU card is desirable because it a computer with a single GPU is inexpensive, almost universally within reach for users looking at genetic data. It is readily available on could-based platforms.

The present invention provides a computer-implemented method for detecting variant nucleic acid from a cell-free nucleic acid-containing sample. The method comprises (a) providing data representing fragment sizes of nucleic acid fragments obtained from said sample and/or representing a measure of deviation from copy number neutrality of the nucleic acid fragments obtained from said sample; b) processing the data from step a) according to a classification algorithm, wherein said classification algorithm operates to classify sample data into one of at least a first class containing the variant nucleic acid and a second class not containing the variant nucleic acid, based on a plurality of cell-free nucleic acid fragment size features and/or a deviation from copy number neutrality feature; and c) outputting the classification of the sample from step b, thereby determining whether the sample contains the variant nucleic acid or not, or a probability that the sample contains the variant nucleic acid. Related methods are also provided.

This disclosure describes a technique for performing random access in a pool of polynucleotides by using one unique primer and one homopolymer primer to selectively amplify some but not all of the polynucleotides in the pool. The polynucleotides are synthesized by a template independent polymerase such as terminal deoxynucleotide transferase (TdT) rather than by phosphoramidite synthesis. Enzymatic synthesis efficiently creates homopolymer sequences through unregulated synthesis. Use of one homopolymer primer instead of two unique primers decreases the complexity, time, and cost of synthesizing the polynucleotides. Use of a unique primer provides a sequence that can be varied to uniquely identify multiple different groups of polynucleotides. This enables random access by polymerase chain reaction (PCR) amplification while still benefitting from the efficiency of homopolymer synthesis. The polynucleotides may include payload regions that use a sequence of nucleotides to encode digital data.