The disclosure describes a method for diagnosing lung cancer in a subject by detecting in a biological sample obtained from that patient a miRNA signature, the presence of which provides an earlier indication of cancer than alternative art-recognized methods, including, but not limited to, low-dose computed tomography (LDCT).

Methods and systems for generating and analyzing genetic variant-phenotype association results are disclosed.

Disclosed in some examples are methods including selecting a first plurality of single gene mutants from a pool of possible single gene mutants of an organism. The first plurality of single gene mutants is less than a number of possible single mutants. A computer processor is used to iteratively select a second plurality of single gene mutants by selecting single gene mutants from the pool of possible single gene mutants that increases a sum of products of similarities between the first plurality of single gene mutants and corresponding functional relationships. The second plurality of single gene mutants is larger in number than the first plurality of single gene mutants, and wherein the second plurality of single gene mutants is less than the number of possible single gene mutants of the organism. A set of genes is outputted comprising the first and second pluralities of single gene mutants.

The present disclosure is based on the surprising and unexpected discovery that a ligand molecule with certain characteristics is able to bind to two protein molecules simultaneously and recruit them to form a transient or stable protein-protein interaction complex. The protein-protein interaction and other cross-domain interactions gained in this process contribute additional stabilization energy to the complex beyond the combination of the binary binding energies, and therefore, largely increase the binding potency of the ligand. Accordingly, the present disclosure provides a Protein-Protein Interaction Inducing Technology (PPIIT), which includes a method to design and identify the tripartite or bifunctional compounds and use such compounds to induce protein-protein interactions in various contexts. The present disclosure also provides a composition for the purpose of inducing protein-protein interactions.

This disclosure presents a model for identifying correlations in genome-wide association studies (GWAS) with function-valued traits that provides increased power and computational efficiency by use of a Gaussian process regression with radial basis function (RBF) kernels to model the function-valued traits and specialized factorizations to achieve speed. A Gaussian Process is assigned to each partition for each allele of a given single nucleotide polymorphism (SNP) which yields flexible alternative models and handles a large number of data points in a way that is statistically and computationally efficient. This model provides techniques for handling missing and unaligned function values such as would occur when not all individuals are measured at the same time points. If the data is complete algebraic re-factorization by decomposition into Kronecker products reduces the time complexity of this model thereby increasing processing speed and reducing memory usage as compared to a naive implementation.

A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

Summarizing an aggregate contribution to a phenotypic characteristic for an individual includes: receiving information pertaining to the phenotypic characteristic of an individual; identifying, using one or more computer processors, a set of one or more markers associated with the phenotypic characteristic; obtaining a set of one or more marker measurements of the individual that corresponds to the set of one or more markers; obtaining a set of one or more statistical factors that measure associations between the set of one or more markers and the phenotypic characteristic; determining an aggregate contribution to the phenotypic characteristic of the individual based at least in part on the retrieved set of one or more statistical factors; and outputting a display characteristic to be displayed that is associated with the aggregate contribution to the phenotypic characteristic for the individual.

A method for determining the likelihood of late ER− breast cancer disease relapse/recurrence is disclosed. Late ER+ breast cancer disease onset and/or recurrence is determined for a period of 5 to 20 years after an initial ER+ breast cancer disease onset in a patient. An ER+ breast cancer patient is assigned a risk score that is compared to a defined threshold value, and identifies the risk score as low risk or high risk for late breast cancer recurrence. A late ER+ breast cancer gene panel of 8 to 15 genes is provided. Subjects having a risk score greater than or equal to that of the threshold value are at a relatively high risk of recurrent disease, and are determined to benefit from aggressive therapeutic intervention, whereas subjects having a risk score less than the threshold value are at a relatively low risk of recurrent disease, and could forego treatment.

Improved molecular breeding methods include a method in which an association data set is developed by associating the phenotypes of a broad population of individuals with the individual genotypes. The association data set is used in conjunction with a growth model in order to select breeding pairs likely to generate offspring with one or more desirable traits.

A protein composition including TorsinA or TorsinA mutant, LULL1, and a nanobody obtained by immunization using TorsinA and LULL1 is used to grow complex crystals, and three dimensional structures are determined using x-ray data of the crystals. A creening platform is built based on the determined three dimensional structures for designing a drug lead to cure dystonia.