Methods, apparatus, system and computer-implemented method are provided for a computer-implemented method of identifying candidate entities of interest associated with disease selection information. The method including: receiving a first set of entities that are predicted to be associated with the disease selection information; retrieving a second set of entities that are known to be associated with the disease selection information; generating a set of entity mappings between entities of the first set of entities, entities the second set of entities, and entities of a graph structure in relation to the disease selection information, the graph structure based on an entity hierarchy, ontology or taxonomy of an entity family associated with the first and second sets of entities, linking entities from the first and second sets of entities to the graph structure based on the generated set of entity mappings; and identifying candidate entities of interest from those linked entities of the first and second sets of entities on the graph structure based on determining where each entity from the first set of entities is located on the graph structure relative to one or more entities of the second set of entities on the graph structure.

Methods, apparatus, system and computer-implemented method are provided for a computer-implemented method of identifying candidate entities of interest associated with disease selection information. The method including: receiving a first set of entities that are predicted to be associated with the disease selection information; retrieving a second set of entities that are known to be associated with the disease selection information; generating a set of entity mappings between entities of the first set of entities, entities the second set of entities, and entities of a graph structure in relation to the disease selection information, the graph structure based on an entity hierarchy, ontology or taxonomy of an entity family associated with the first and second sets of entities, linking entities from the first and second sets of entities to the graph structure based on the generated set of entity mappings; and identifying candidate entities of interest from those linked entities of the first and second sets of entities on the graph structure based on determining where each entity from the first set of entities is located on the graph structure relative to one or more entities of the second set of entities on the graph structure.

The present invention relates to a method and a kit, a computer-implemented method and a system for in vitro predicting survival time of an individual with bladder cancer after surgery from an individual's biological sample. Expression levels of a target gene combination of in vitro aggressive bladder cancer specimen of a patient are detected, and the target gene combination includes at least two of PPT2, ARMH4, P4HB, SLC1A6 and ARID3A, a fragment, a homologue, a variant and a derivative thereof. Next, the expression levels are respectively compared with the reference expression levels of a reference database, and converted to a risk score sum, thereby predicting an averaged survival time of a patient having aggressive bladder cancer after surgery, and being beneficially applied to a kit and a computer-implemented method for in vitro predicting survival time of patient with most aggressive types of bladder cancer after surgery.

The present invention relates to a method and a kit, a computer-implemented method and a system for in vitro predicting survival time of an individual with bladder cancer after surgery from an individual's biological sample. Expression levels of a target gene combination of in vitro aggressive bladder cancer specimen of a patient are detected, and the target gene combination includes at least two of PPT2, ARMH4, P4HB, SLC1A6 and ARID3A, a fragment, a homologue, a variant and a derivative thereof. Next, the expression levels are respectively compared with the reference expression levels of a reference database, and converted to a risk score sum, thereby predicting an averaged survival time of a patient having aggressive bladder cancer after surgery, and being beneficially applied to a kit and a computer-implemented method for in vitro predicting survival time of patient with most aggressive types of bladder cancer after surgery.

Provided are a method of detecting chromosomal aneuploidy of a targeted fetal chromosome, and a computer-readable medium having recorded thereon a program to be applied to performing the method. According to the present disclosure, fetal chromosomal aneuploidy may be non-invasively and prenatally diagnosed with excellent sensitivity and specificity.

Provided are a method of detecting chromosomal aneuploidy of a targeted fetal chromosome, and a computer-readable medium having recorded thereon a program to be applied to performing the method. According to the present disclosure, fetal chromosomal aneuploidy may be non-invasively and prenatally diagnosed with excellent sensitivity and specificity.

The present invention provides a novel method for the classification of adjuvants. In one embodiment, the present invention provides a method for generating organ transcriptome profiles for adjuvants, said method comprising: (A) a step for obtaining expression data by performing transcriptome analysis for at least one organ of a target organism by using at least two adjuvants; (B) a step for clustering the adjuvants with respect to the expression data; and (C) a step for generating the organ transcriptome profile for the adjuvants on the basis of the clustering.

The present invention provides a novel method for the classification of adjuvants. In one embodiment, the present invention provides a method for generating organ transcriptome profiles for adjuvants, said method comprising: (A) a step for obtaining expression data by performing transcriptome analysis for at least one organ of a target organism by using at least two adjuvants; (B) a step for clustering the adjuvants with respect to the expression data; and (C) a step for generating the organ transcriptome profile for the adjuvants on the basis of the clustering.

The disclosure provides gene fusions, gene variants, and novel associations with disease states, as well as kits, probes, and methods of using the same.

Compositions, methods and biomarkers for diagnostics, monitoring and therapy of various health and complex-disease conditions, such as malignant and neurodegenerative disorders, utilizing the techniques of data mining, computational biology, artificial intelligence and molecular biology are provided.