Systems and methods for developing predictive models are provided that consider the mechanistic regulations of various bimolecular events and measure the expression of various genes, proteins, and metabolites. The predictive models are utilized in a computer-implemented method to classify the patient derived glioma tumor samples in different pathological grades and successively predict the patient specific combination of drug targets by analyzing the intra-tumor heterogeneity.

Methods, systems, and software provide machine learning and artificial intelligence including deep neural networks that enable the creation and operation of unique, AI-driven genomic test results augmentation through variable genetic imputation.

Methods, systems, and software provide machine learning and artificial intelligence including deep neural networks that enable the creation and operation of unique, AI-driven genomic test results augmentation through variable genetic imputation.

A device made from a hydrogel matrix is provided. The hydrogel matrix includes a photoabsorber with a void architecture in the matrix, having a first vessel architecture and a second vessel architecture that are each tubular and branching, wherein the first and second vessel architectures are fluidically independent from each other. A pre-polymerization solution for forming the device, and methods of fabricating such devices are described. A method of fabricating a 3D hydrogel construct is provided. The method includes using a computer-implemented process to create a 3D model of the construct based on a tessellation of polyhedra having a number of faces connected by edges and vertices, generate a first vascular component of the model, generate a second vascular component of the model, and combine the first and second vascular components of the model.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

A method is provided for determining at least one candidate reactant. One embodiment of this method includes the following steps: forming by a computer processor a graph of known reactants and known products, the graph comprising links between the known reactants and their known products, receiving by a computer processor the target compound, determining by a computer processor whether the graph includes the target compound and adding the target compound to the graph if the target compound was not previously included, forming by a computer processor a matrix representing at least a portion of the known reactants, a portion of the known products and the target compound, providing a matrix value of the graph by a computer processor for one or more candidate reactants and determining by a computer processor at least one link in the graph between the target compound and the candidate reactant based on matrix values.

Methods for creating a model that is a graphical representation are provided. In one aspect, a method includes including receiving a first dataset including a first variable and a third variable, and a second dataset including a second variable and the third variable. The method also includes creating graphical representations of the first and second datasets by applying conditional independence tests on them, and storing conditional independence information obtained by applying the conditional independence tests on the first and second datasets. The method also includes applying a bivariate causal discovery algorithm. The method further includes modifying the graphical representations of the first and second dataset according to the determined causal relations, and creating a set of candidate graphical representations for a third dataset including the first and second datasets. Each candidate graphical representation is consistent with the conditional independence information. Systems and machine-readable media are also provided.

A method includes receiving proteomic data for each of a plurality of identified post-translational modification (PTM) sites. A pairwise correlation of co PTM is computed among each respective pair of the identified PTM sites. The method also includes generating a co-PTM network based on each pairwise correlation to describe co PTM characteristics for the identified PTM sites. The method also includes predicting enzyme-substrate associations for each of the plurality of identified PTM sites based on the co-PTM network and a set of predetermined enzyme-substrate associations.

The invention provides oncogenomic methods for detecting tumors by identifying circulating tumor DNA. A patient-specific reference directed acyclic graph (DAG) represents known human genomic sequences and non-tumor DNA from the patient as well as known tumor-associated mutations. Sequence reads from cell-free plasma DNA from the patient are mapped to the patient-specific genomic reference graph. Any of the known tumor-associated mutations found in the reads and any de novo mutations found in the reads are reported as the patient's tumor mutation burden.