Abstract: A system and method for determining the functional impact of somatic mutations and genomic aberrations on downstream cellular processes by integrating multi-omics measurements in cancer samples with community-curated biological pathways are disclosed.

Abstract: Methods, systems and apparatus for detecting patterns in constituents of at least one biological organism are disclosed. In accordance with one method, clusters of the constituents are determined (208) by selecting (210) different subsets of at least one of genes or proteins and identifying (212) the clusters from biological data corresponding to the selected subsets. Here, membership values for the constituents, indicating membership within the clusters, are calculated for use as a basis of an additional cluster determination process (208) to obtain final clusters of constituents. By underpinning the preliminary clustering on different subsets of biological data and formulating the higher-level clustering on the basis of the membership values, the embodiments can enable an evaluation of a large variety of biological data in a practical, accurate and highly efficient manner.

Abstract: The present disclosure describes systems and methods for generating a priority score for a variant of a gene based on its potential significance to a disease. Priority scores may be calculated for multiple variants, and the variants may be ranked based on the generated priority scores.

Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.

Abstract: Methods, systems and apparatus for detecting subpopulations of constituents of at least one biological organism are disclosed. In accordance with exemplary embodiments, biological data compiled from a cohort of the constituents of at least one biological organism is formulated (112) as a set of discrete-time real valued vector signals. Further, frequency domain analysis is performed (114) on the vector signals of the biological data to compile spectral properties of the vector signals. The spectral properties can be employed to efficiently detect subpopulations of the cohort while maintaining a high degree of accuracy.

Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.

Abstract: A method of identifying co-expressed coding and noncoding genes is disclosed. The method may include receiving genetic sequences, mapping the genetic sequences to known coding and noncoding genes, correlating the mapped genes, and generating a co-expression network. A system for generating a co-expression network and providing the co-expression network to a user on a display is disclosed. The system may include a memory, one or more processors, one or more databases, and a display.

Abstract: An imaging visualization workstation (30) includes a graphical display device (32) and an electronic data processor, and is configured to perform a method including: spatially registering a biopsy sample extracted from a medical subject with a medical image (12) of the medical subject; combining the medical image with a graphical representation of information (20, 22) generated from the biopsy sample to generate a combined image in which the graphical representation is spatially delineated based on the spatial registration of the biopsy sample; and displaying the combined image on the graphical display device of the imaging visualization workstation. A method comprises extracting a biopsy sample spatial sample from a medical subject, processing the biopsy sample to generate biopsy information, acquiring a medical image of the subject, spatially registering the biopsy sample with the medical image, and displaying the medical image modified to include an annotation generated from the biopsy information.

Abstract: Methods and systems for visualizing gene expression data in a way that permits the comparison of different patient groups to facilitate medical applications, including cancer diagnostics and treatment planning, particularly breast cancer. The method organises gene expression data for at least one patient into a plurality of windows of a specified size, calculates an average RSEM score for all of the genes in each window and presents the average RSEM scores in a two-dimensional array, wherein one axis organises the windows by patient and the other axis organises the windows by sequence.

Abstract: Methods and systems for identifying causal genetic mechanisms of antibiotic resistance in pathogens. In accordance with at least one embodiment, the system includes a gene resistance module to identify genes present in an antibiotic resistant pathogen, a single nucleotide polymorphism module to identify mutations present in an antibiotic resistant pathogen, and an antibiotic resistance module configured to output the causation of antibiotic resistance based on the identified genes and mutations.

Abstract: The amount of genomic data as well the sensitivity of the information carried necessitates the need to develop smart and efficient ways to transmit genomic data in a secure way. While encryption schemes exist, there is also the need to first reduce the amount of massive information and then apply an encoding and encryption method that will be effective both in the economic sense as well as for security of genomic data. In this invention, we discuss novel techniques to encode processed variant information and send it across to a remote site ensuring covert transmission. The protocols not only encode and encrypts the information; it condenses the information that needs to be transferred.

Abstract: Data-driven generalized regression-based frameworks that support the transformation of measurements, applicable but not limited to gene expressions, from one platform to another over a wide dynamic range, with selected summary statistics/feature values as predictors for the model parameters. The framework consists of primary model training and transformation, and additional levels of categorical regression and transformation processes.

Abstract: Methods and apparatus for a secure framework for storing and analyzing genomic data. Embodiments of the present invention apply persistent governance to sensitive information and to the analytics that operate upon it, managing the interaction between the two.

Abstract: The present disclosure describes systems and methods for generating a priority score for a variant of a gene based on its potential significance to a disease. Priority scores may be calculated for multiple variants, and the variants may be ranked based on the generated priority scores.

Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.

Abstract: The present invention relates to a method of non-invasively monitoring the expression of a gene of interest in a cell when contacting said cell with a compound influencing the expression of said gene of interest. The present invention is also concerned with different isolated nucleic acid molecules comprising a coding sequence. Said coding sequence comprises a gene of interest-sequence encoding a gene of interest-polypeptide fused to a reporter sequence encoding a fluorescent reporter polypeptide and is operatively coupled to a promoter sequence. The present invention is also concerned with the use of a method and a nucleic acid molecule of the invention for delivering a compound influencing the expression of a gene of interest in a cell, monitoring the delivery of said compound as well as monitoring the influence on the expression of said gene of interest induced by said compound at the same time.

Abstract: Clinical decision support visualization methods that use information, pathways, or inferred regulatory networks for the entire genome, transcriptome, exome, or methylome to highlight genomic activity to further the understanding of the clinical condition of a patient or to contrast different patient groups.

Abstract: In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-? signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample (70) of a breast tumor of a patient acquired before initiating (71) a breast cancer therapy regimen to the patient and response gene expression level information from a response sample (72) of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-? signaling pathway. Related predictive outcome assessment test development methods are also disclosed.

Abstract: Image texture feature values are computed for a set of image texture features from an image of an anatomical feature of interest in a subject, and the subject is classified respective to a molecular feature of interest based on the computed image texture feature values. The image texture feature values may be computed from one or more gray level co-occurrence matrices (GLCMs), and the image texture features may include Haralick and/or Tamura image texture features. To train the classifier, reference image texture feature values are computed for at least the set of image texture features from images of the anatomical feature of interest in reference subjects. The reference image texture feature values are divided into different population groups representing different values of the molecular feature of interest, and the classifier is trained to distinguish between the different population groups based on the reference image texture feature values.

Abstract: Relevance of a study genetic variant observed in diagnostic subject genetic data that is associated by a clinical study with a phenotype characteristic is assessed as follows. A set of polymorphisms functionally related to the study genetic variant are identified. A foreground distribution is computed of variants observed in the diagnostic subject genetic data for the set of polymorphisms. A background distribution is computed of variants observed in genetic data of subjects of the clinical study for the set of polymorphisms. A comparison metric is computed comparing the foreground distribution and the background distribution. Relevance of the study variant to the diagnostic subject is quantified based on the comparison metric, with higher similarity of the foreground and background distributions corresponding to higher relevance.