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.

Systems and methods for machine learning tissue classification are provided herein. In one embodiment, a system includes a storage element operable to store datasets of a plurality of biological samples. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. A processor is operable to train a machine learning model with the stored datasets to learn molecular measurements of the biological samples. The processor may then process an image from another biological sample through the trained machine learning module to predict molecular measurement data of the other biological sample.

The present invention relates to peptides, proteins, nucleic acids and cells for use in immunotherapeutic methods. In particular, the present invention relates to the immunotherapy of cancer. The present invention furthermore relates to tumor-associated T-cell peptide epitopes, alone or in combination with other tumor-associated peptides that can for example serve as active pharmaceutical ingredients of vaccine compositions that stimulate anti-tumor immune responses, or to stimulate T cells ex vivo and transfer into patients. Peptides bound to molecules of the major histocompatibility complex (MHC), or peptides as such, can also be targets of antibodies, soluble T-cell receptors, and other binding molecules.

The disclosure provides for artificial signal peptides generated by systems and methods utilizing deep learning.

The disclosure provides for artificial signal peptides generated by systems and methods utilizing deep learning.

Described herein are methods of determining segregation dynamics of mitochondrial DNA herein. Also described herein are methods of diagnosing, prognosing, and/or monitoring a mitochondrial disease.

Systems and methods for determining diagnostic and/or prognostic risk of having or developing brain injury related symptoms after a head impact includes a point-of-care assay reader, a PHI smart device application, and a neurocognitive/vestibular smart device application. The diagnosis and prognosis server application includes instructions stored on a non-transitory computer-readable medium executed on the server that receives patient protected health information (PHI) from the PHI smart device application, receives neurocognitive test results from a neurocognitive testing application, and receives assay results from the point-of-care assay reader and generates a diagnostic score and a prognostic risk scores for post-acute traumatic brain injury TBI symptom categories as measures of patient outcomes.

Systems and methods for determining diagnostic and/or prognostic risk of having or developing brain injury related symptoms after a head impact includes a point-of-care assay reader, a PHI smart device application, and a neurocognitive/vestibular smart device application. The diagnosis and prognosis server application includes instructions stored on a non-transitory computer-readable medium executed on the server that receives patient protected health information (PHI) from the PHI smart device application, receives neurocognitive test results from a neurocognitive testing application, and receives assay results from the point-of-care assay reader and generates a diagnostic score and a prognostic risk scores for post-acute traumatic brain injury TBI symptom categories as measures of patient outcomes.

Cumulus cell (CC) gene expression is being explored as an additional method to morphological scoring to choose the embryo with the highest chance to pregnancy. The present invention relates to a novel method of identifying biomarker genes for evaluating the competence of a mammalian oocyte in giving rise to a viable pregnancy after fertilization, based on the use of live birth and embryonic development as endpoint criteria for the oocytes to be used in an exon level analysis of potential biomarker genes. The invention further provides CC-expressed biomarker genes thus identified, as well as prognostic models based on the biomarker genes identified using the methods of the present invention.

Cumulus cell (CC) gene expression is being explored as an additional method to morphological scoring to choose the embryo with the highest chance to pregnancy. The present invention relates to a novel method of identifying biomarker genes for evaluating the competence of a mammalian oocyte in giving rise to a viable pregnancy after fertilization, based on the use of live birth and embryonic development as endpoint criteria for the oocytes to be used in an exon level analysis of potential biomarker genes. The invention further provides CC-expressed biomarker genes thus identified, as well as prognostic models based on the biomarker genes identified using the methods of the present invention.