The present invention relates to methods for evaluating the probability that a patient's diagnosis may be treated with a particular clinical regimen or therapy.

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 by selecting different subsets of at least one of genes or proteins and identifying 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 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.

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 by selecting different subsets of at least one of genes or proteins and identifying 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 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.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

The present invention provides a method for compressing genome sequences readers using GPU processing unit. The method comprising the steps of: identifying position of each given genome reader characters string in the sequence of a reference genome, determining alignment of each reader string within the reference genome, comparing each reader characters string to corresponding reference genome sequence based on determined alignment, filtering characters in each reader by GPU processor by eliminating similar characters and extracting only characters differences in association to their position in the genome sequence and recording filtered data of each reader in association to its alignment in genome reference at the genome compressed database.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.

Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.