The present application mainly relates to specific methods for inferring activity of a cellular signaling pathway in tissue and/or cells of a medical subject based at least on expression levels of one or more target gene(s) of the cellular signaling pathway measured in an extracted sample of the tissue and/or cells of the medical subject, an apparatus comprising a digital compressor configured to perform such methods and a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such methods.

The present application mainly relates to specific methods for inferring activity of a cellular signaling pathway in tissue and/or cells of a medical subject based at least on expression levels of one or more target gene(s) of the cellular signaling pathway measured in an extracted sample of the tissue and/or cells of the medical subject, an apparatus comprising a digital compressor configured to perform such methods and a non-transitory storage medium storing instructions that are executable by a digital processing device to perform such methods.

Methods and systems for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.

The invention describes the method allowing for efficient predictive ranking of clinical efficiencies of the existing targeted medicinal products for individual patient with proliferative or oncology disease. The method makes it possible to use a wide range of experimental data received from the patients' pathological tissue samples and relevant control samples: information on gene mutations, transcription factor binding profile, protein (considering harmonization), mRNA (considering harmonization) and microRNA expression strength. The method also uses information on molecular targets of the medicinal products. This method can be automated to prevent potential errors associated with manual calculation and makes it possible to consider patient-specific changes in hundreds and thousands molecular pathways which include tens and hundreds of gene products. This method also considers the features and mode of action of various classes of target drugs. Using this method will enable selecting a medicinal product for the patient based on the analysis of objective individual changes occurred in the pathological tissue.

The present invention relates to extraction of a signal for a target nucleic acid sequence from signals for two target nucleic acid sequences in a sample. The present invention can contribute to dramatic improvement in methods for detecting target nucleic acid sequences using different detection temperatures and reference values. The present invention using an amended reference value as well as an initial reference value can lead to increasing the detection accuracy in methods for detecting target nucleic acid sequences using different detection temperatures and reference values.

The present invention relates to extraction of a signal for a target nucleic acid sequence from signals for two target nucleic acid sequences in a sample. The present invention can contribute to dramatic improvement in methods for detecting target nucleic acid sequences using different detection temperatures and reference values. The present invention using an amended reference value as well as an initial reference value can lead to increasing the detection accuracy in methods for detecting target nucleic acid sequences using different detection temperatures and reference values.

This disclosure provides methods, systems, compositions, and kits for the multiplexed detection of a plurality of analytes in a sample. In some examples, this disclosure provides methods, systems, compositions, and kits wherein multiple analytes may be detected in a single sample volume by acquiring a cumulative measurement or measurements of at least one quantifiable component of a signal. In some cases, additional components of a signal, or additional signals (or components thereof) are also quantified. Each signal or component of a signal may be used to construct a coding scheme which can then be used to determine the presence or absence of any analyte.

This disclosure provides methods, systems, compositions, and kits for the multiplexed detection of a plurality of analytes in a sample. In some examples, this disclosure provides methods, systems, compositions, and kits wherein multiple analytes may be detected in a single sample volume by acquiring a cumulative measurement or measurements of at least one quantifiable component of a signal. In some cases, additional components of a signal, or additional signals (or components thereof) are also quantified. Each signal or component of a signal may be used to construct a coding scheme which can then be used to determine the presence or absence of any analyte.

A computer based biological sequence analysis method provides, after a training phase adopting data from screening experiments, either an evaluation of an input sequence expressing the performance with reference to the chemical-physical feature object of the screening experiment, or at least an optimized output sequence. The method provides the use of a set or library of sequences derived from DMS experiments and SELEX for the generation of a second set of high efficiency biological sequences, whereby high efficiency means, for example, high catalysis capacity, high fitness, high ability to bind to a specific target, high fluorescence activity and, in general, a high performance with reference to the chemical-physical properties of a molecule which are defined at the start and can be selected through experiments.

A system and method for identification of key driver responsible for bringing changes in a microbial population is provided. The method involves construction of microbial association networks with each microbial taxa as nodes and their associations as edges and subsequent identification of crucial ‘driver’ nodes involved in the studied disease progression. While comparing a particular node between two networks, this method takes individual nodes and their associations into account as well as the identity of their interacting partners. A taxon in the diseased state with an altered set of associations while still being increasingly important for the whole network necessarily holds a key significance in microbial interplay. Using this rationale, this methodology computes a score to quantify this change for each node and calculates its statistical significance. Subsequently, ‘driver’ nodes are identified using the score coupled with other network parameters and a critical score for the ‘driver’ nodes is calculated to quantify its importance.