Abstract: The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Abstract: The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

Abstract: The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

Abstract: The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

Abstract: Disclosed are methods and systems for detecting methylation of the promoter of O-6-methylguanine-DNA methyltransferase gene (MGMT). In particular, the methods and systems may be utilized to detect methylation in the MGMT promoter in a DNA sample from a glioblastoma and optionally in order to predict whether a subject having the glioblastoma will respond to treatment with an alkylating agent. The methods and systems typically include a step of deep-sequencing the DNA sample after the DNA sample has been treated with a reagent that converts unmethylated cytosine to uracil such as a bisulfite reagent.

Abstract: Methods and tools are provided for detecting and predicting lung cancer. The methods and tools are based on epigenetic modification due to methylation of genes in lung cancer or pre-lung cancer. The tools can be assembled into kits or can be used separately. Genes found to be epigenetically silenced in association with lung cancer include ACSL6, ALS2CL, APC2, ARTS-1, BEX1, BMP7, BNIP3, CBR3, CD248, CD44, CHD5, DLK1, DPYSL4, DSC2, EDNRB, EPB41L3, EPHB6, ERBB3, FBLN2, FBN2, FOXL2, GNAS, GSTP1, HS3ST2, HPN, IGFBP7, IRF7, JAM3, LOX, LY6D, LY6K, MACF1, MCAM, NCBP1, NEFH, NID2, PCDHB15, PCDHGA12, PFKP, PGRMC1, PHACTR3, PHKA2, POMC, PRKCA, PSEN1, RASSF1A, RASSF2, RBP1, RRAD, SFRP1, SGK, SOD3, SOX17, SULF2, TIMP3, TJP2, TRPV2, UCHL1, WDR69, ZFP42, ZNF442, and ZNF655.

Abstract: Disclosed are methods for detecting expression and/or aberrant methylation patterns in genes such as the gene DCR1 and their potential to diagnose or prognose a cancer or to predict drug resistance/susceptibility. More specifically, the disclosure relates to oligonucleotides, primers, probes, primer pairs and kits to detect genes such as the gene DCR1, and in particular, methylated forms of genes such as the gene DCR1. The disclosure also relates to pharmacogenetic methods to diagnose or prognose a cancer, to determine suitable treatment regimens for cancer, and to determine methods for treating cancer patients based on expression and/or aberrant methylation patterns in genes such as the gene DCR1.

Abstract: A method for personal genome information management includes receiving personal genome sequence data at a mobile device. The personal genome sequence data is compared to a reference genome sequence data to identify one or more sequence variants from the personal genome sequence data. One or more sequence variants from the personal genome sequence data are assigned to categories of hierarchical lists. One or more visual displays are provided to the user based upon the assignment of the sequence variants in the categories of hierarchical lists.

Abstract: Methods and tools are provided for detecting and predicting lung cancer. The methods and tools are based on epigenetic modification due to methylation of genes in lung cancer or pre-lung cancer. The tools can be assembled into kits or can be used seperately. Genes found to be epigentically silenced in association with lung cancer include ACSL6, ALS2CL, APC2, ART-S1, BEX1, BMP7, BNIP3, CBR3, CD248, CD44, CHD5, DLK1, DPYSL4, DSC2, EDNRB, EPB41L3, EPHB6, ERBB3, FBLN2, FBN2, FOXL2, GNAS, GSTP1, HS3ST2, HPN, IGFBP7, IRF7, JAM3, LOX, LY6D, LY6K, MACF1, MCAM, NCBP1, NEFH, NID2, PCDHB15, PCDHGA12, PFKP, PGRMC1, PHACTR3, PHKA2, POMC, PRKCA, PSEN1, RASSF1A, RASSF2, RBP1, RRAD, SFRP1, SGK, SOD3, SOX17, SULF2, TIMP3, TJP2, TRPV2, UCHL1, WDR69, ZFP42, ZNF442, and ZNF655.