Abstract: Systems and methods of the disclosure may include a computer-implemented method, the computer-implemented method including: receiving, at a computer system, nucleic acid sequencing data derived from a methylation assay performed on a biological sample associated with at least one subject; computing, using a processor associated with the computer system, a beta value matrix based on the nucleic acid sequencing data, wherein the beta value matrix comprises one or more missing beta values; addressing, using the processor, the one or more missing beta values in the beta value matrix using a missing beta value completion approach; identifying, using the processor, one or more principal components in the completed beta value matrix; and training, using the one or more principal components in combination with a predetermined set of clinical variables, a classifier to predict a survival outcome for a target subject associated with a disease type.

Abstract: Gene copy number variations are identified for genes in a targeted gene panel. For each gene, coverage at each base position across the gene is determined. The coverage at each base position can be influenced by the hybridization probes that are used to determine the base level coverage of the base position. The base level coverage for each base position is normalized to account for the characteristics of the hybridization probes. To determine whether a copy number variation exists for a gene, the base level coverage of base positions across the gene for a subject is analyzed to determine whether it deviates from the base level coverage of base positions across the gene for previously analyzed, healthy individuals. If a significant deviation exists, a copy number variation for the gene is called.

Abstract: Methods and systems for segmenting sequencing regions obtained from a sample interval are disclosed. sample contamination detection are disclosed. In particular, an analytics system accesses test sequences from a sample. The test sequences each include a sequencing region which, in aggregate, form an aggregate sequencing region. The analytics system segments sequencing regions from the aggregate sequencing region into sequencing subregions. Several methods of segmenting sequencing regions into sequencing subregions are disclosed: (1) maximizing cancer vs. non-cancer methylation beta differences, (2) minimizing cancer vs. non-cancer methylation beta differences, (3) segmentation based on CpG density in regions, (4) dynamic generation of sequencing subregions based on mutual information scores and cancer classification propensity. The analytics system applies selects sequencing subregions and applies a cancer classifier to those subregions to identify cancer presence in the sample.

Abstract: Methods are provided to improve the positive predictive value for cancer detection using cell-free nucleic acid samples. The methods can include the use of at least two assays. The assays can vary, for example, with respect to sensitivity, specificity, sequencing depth, analyte, and cost. An exemplary method can be used to provide an initial cancer assay with high sensitivity and a follow-up assay with high specificity in detecting cancer.

Abstract: An analytics system creates a data structure counting strings of methylation vectors from a healthy control group. The analytics system enumerates possibilities of methylation state vectors given a sample fragment from a subject, and calculates probabilities for all possibilities with a Markov chain probability. The analytics system generates a p-value score for the subject's test methylation state vector by summing the calculated probabilities that are less than or equal to the calculated probability of the possibility matching the test methylation state vector. The analytics system determines the test methylation state vector to be anomalously methylated compared to the healthy control group if the p-value score is below a threshold score. With a number of such sample fragments, the analytics system can filter the sample fragments based on each p-value score. The analytics system can run a classification model on the filtered set to predict whether the subject has cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for diagnosis of cancer.

Abstract: Aspects of the invention relate to methods for preparing and analyzing a sequencing library from a mixed cell-free DNA (cfDNA) sample, wherein the mixed sample includes double-stranded DNA (dsDNA), damaged dsDNA (e.g., nicked dsDNA), and single-stranded DNA (ssDNA) molecules. The subject methods facilitate the collection of information from dsDNA, ssDNA and damaged DNA (e.g., nicked DNA) molecules in a sample, thereby providing enhanced diagnostic information as compared to sequencing libraries that are prepared from dsDNA alone.

Abstract: Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.

Abstract: Cross-contamination of a test sample used to determine cancer is identified using gene sequencing data. Each test sample includes a number of test sequences that may include a single nucleotide polymorphism (SNP) that can be indicative of cancer. The test sequences are be filtered to remove or negate at least some of the SNPs from the test sequences. Negating the test sequences allows more test sequences to be simultaneously analyzed to determine cross-contamination. Cross-contamination is determined by modeling the variant allele frequency for the test sequences as a function of minor allele frequency, contamination level, and background noise. In some cases, the variant allele frequency is based on a probability function including the minor allele frequency. Cross-contamination of the test sample is determined if the determined contamination level is above a threshold and statistically significant.

Abstract: Diagnostic devices and methods are provided for screening for a disease condition, including a cancer condition or a mendelian disease. The diagnostic devices allow for in vivo contact of cell-free nucleic acids or circulating tumor cells. The diagnostic device has a needle with a body and a detection reaction module attached to the body.

Abstract: A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.

Abstract: Systems and methods for predicting survival outcomes in patients diagnosed with Myelodysplastic Syndrome (MDS) are disclosed. One method may include: receiving DNA sequencing data derived from a methylation assay performed on a biological sample associated with the at least one patient; computing methylation beta-values for one or more CpG-sites identified in the sequencing data; identifying one or more differentially methylated regions (DMRs) based on statistical analysis of the methylation beta-values for the one or more CpG-sites; selecting, via a feature selection process, a subset of the one or more DMRs to utilize as training data; and training, using the training data, the classifier to predict the survival outcome of the at least one patient. Other aspects are described and claimed.

Abstract: Systems and methods for determining a cancer class of a subject are provided in which a plurality of sequence reads, in electronic form, are obtained from a biological sample of the subject. The sample comprises a plurality of cell-free DNA molecules including respective DNA molecules longer than a threshold length of less than 160 nucleotides. The plurality of sequence reads excludes sequence reads of cell-free DNA molecules in the plurality of cell-free DNA molecules longer than the threshold length. The plurality of sequence reads is used to identify a relative copy number at each respective genomic location in a plurality of genomic locations in the genome of the subject. The genetic information about the subject obtained from the sample and the genetic information consisting of the identification of the relative copy number at each respective genomic location, is applied to a classifier that determines the cancer class of the subject.

Abstract: A method for extracting information from a dataset, e.g., a document, includes: receiving the dataset at an information handling device, optionally, extracting, via optical character recognition implemented by a processor of the information handling device, textual information associated with the dataset, and classifying the dataset into one of a plurality of classes. Classifying the dataset may include computing a similarity score for each of the plurality of classes for each of a plurality of window regions of the dataset, calculating a subset of highest similarity scores for each of the plurality of classes for each of the plurality of window regions, determining overall similarity scores for each of the plurality of classes, and classifying the dataset as corresponding to a class with a highest overall similarity score.

Abstract: In comparison to conventional sequencing pileup algorithms, the process described herein generates sequencing pileups that contains additional information not typically reported by conventional algorithms while also consuming fewer computational resources (e.g., time, processing power, and memory). First, each of a FASTA reference genome and BAM sequence read files are converted to an internal representation. This enables the rapid iteration across nucleotide bases of the sequence reads to determine support characteristics that summarize information of nucleic acid molecules corresponding to positions across the reference genome. Next, the support characteristics of positions across the reference genome are stored through a memory allocation process that utilizes a first and a second temporary storage. This enables the convenient freeing of one temporary storage while the other temporary storage is being used.

Abstract: Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Abstract: Systems and methods for detecting a subtype of a disease state and for determining the development of a resistance mechanism in a disease are disclosed. One method may include: receiving, at an input component of the system, a set of sequence reads associated with a nucleic acid sample; generating, using a processor of the system and via analysis of the set of sequence reads, methylation data; and analyzing, using the processor, the methylation data to identify the subtype of the disease state. Another method may include: obtaining methylation data from a targeted methylation sequencing assay, applying the methylation data to a trained machine learning model, and receiving an output indicating whether MRD is present in a test subject and/or whether a resistance mechanism has been developed by a disease. Other aspects are described and claimed.

Abstract: Provided herein are compositions comprising tissue-specific markers for identifying a tissue of origin of a cell-free nucleic acid, e.g., a cell-free DNA molecule. Also provided herein are methods, compositions, and systems for identifying a tissue of origin of a cell-free nucleic acid by determining an absolute amount of cell-free nucleic acids comprising the tissue-specific marker. Also provided herein are methods, compositions, and systems for detecting a cancer in a tissue of an organism by analyzing tissue-specific markers.

Abstract: Systems and methods for classifier training are provided. A first dataset is obtained that comprises, for each first subject, a corresponding plurality of bin values, each for a bin in a plurality of bins, and subject cancer condition. A feature extraction technique is applied to the first dataset thereby obtaining feature extraction functions, each of which is an independent linear or nonlinear function of bin values of the bins. A second dataset is obtained comprising, for each second subject, a corresponding plurality of bin values, each for a bin in the plurality of bins and subject cancer condition. The plurality of bin values of each corresponding subject in the second plurality are projected onto the respective feature extraction functions, thereby forming a transformed second dataset comprising feature values for each subject. The transformed second dataset and subject cancer condition serves to train a classifier on the cancer condition set.

Abstract: Methods for preparing enriched sequencing libraries from test samples that contain double-stranded deoxyribonucleic acid (dsDNA) are provided.