The present invention includes methods and kits for the diagnosing a neurological disease within primary care settings comprising: obtaining a blood test sample from a subject, measuring IL-7 and TNFα biomarkers in the blood sample, comparing the level of the one or a combination of biomarkers and neurocognitive screening tests with the level of a corresponding one or combination of biomarkers in a normal blood sample and neurocognitive screening tests, and predicting that an increase in the level of the blood test sample in relation to that of the normal blood sample indicates that the subject is likely to have a neurological disease.

The present invention includes methods and kits for the diagnosing a neurological disease within primary care settings comprising: obtaining a blood test sample from a subject, measuring IL-7 and TNFα biomarkers in the blood sample, comparing the level of the one or a combination of biomarkers and neurocognitive screening tests with the level of a corresponding one or combination of biomarkers in a normal blood sample and neurocognitive screening tests, and predicting that an increase in the level of the blood test sample in relation to that of the normal blood sample indicates that the subject is likely to have a neurological disease.

Diagnostic and prognostic assays and a portable point of care system is provided that performs such assays using an automated, artificial intelligence (AI) based molecular analyses of a subject sample. The system provides for rapid, cost-efficient multiplexable assessment of biomarker panels in a cell sample and may be easily used for global and remote applications.

The present invention is to provide a multi-class classification method, a multi-class classification program, and a multi-class classification device which select a feature amount and classify a sample into any of a plurality of classes based on a value of the selected feature amount, and a feature amount selection method, a feature amount selection device, and a feature amount set which are used for such multi-class classification. The present invention handles a multi-class classification problem involving feature amount selection. The feature amount selection is a method of literally selecting in advance a feature amount needed for each subsequent processing (particularly, the multi-class classification in the present invention) from among a large number of feature amounts included in a sample. The multi-class classification is a discrimination problem that decides which of a plurality of classes a given unknown sample belongs to.

The present invention is to provide a multi-class classification method, a multi-class classification program, and a multi-class classification device which select a feature amount and classify a sample into any of a plurality of classes based on a value of the selected feature amount, and a feature amount selection method, a feature amount selection device, and a feature amount set which are used for such multi-class classification. The present invention handles a multi-class classification problem involving feature amount selection. The feature amount selection is a method of literally selecting in advance a feature amount needed for each subsequent processing (particularly, the multi-class classification in the present invention) from among a large number of feature amounts included in a sample. The multi-class classification is a discrimination problem that decides which of a plurality of classes a given unknown sample belongs to.

Processes and materials to detect cancer, transplant rejection, or fetal genetic abnormalities from a biopsy are described. In some cases, nucleic acid molecules, such as cell-free nucleic acids, can be sequenced, and the sequencing result can be utilized to detect sequences indicative of a neoplasm, transplant rejection, or fetal genetic abnormality. Detection of somatic variants occurring in phase and/or insertions and deletions (indels) can indicate the presence of cancer, transplant rejection, or fetal genetic abnormalities in a diagnostic scan, and a clinical intervention can be performed.

A system and method for region-based calling utilizes a probability distribution of a phi-transformed logarithmic ratio to determine a set of possible transition paths through markers and marker states, constructs a local evidence matrix for each of the markers and generates a total per-marker value for each segment in a discrete region.

A system and method for region-based calling utilizes a probability distribution of a phi-transformed logarithmic ratio to determine a set of possible transition paths through markers and marker states, constructs a local evidence matrix for each of the markers and generates a total per-marker value for each segment in a discrete region.

The present disclosure is directed to methodologies or technologies for generating a predictor of a disease state (e.g. cancer-therapy efficacy status, cancer therapy progress, cancer prognosis, cancer diagnosis, therapy failure, relapse, recurrence, and the like) based on genomic and proteomic signatures, gene expression, and pathways & networks activation of endogenous human stem cell-associated retroviruses (SCAR). This disclosure is also directed to methods of targeting, designing, and using treatments for clinically intractable malignant tumors.

The present disclosure is directed to methodologies or technologies for generating a predictor of a disease state (e.g. cancer-therapy efficacy status, cancer therapy progress, cancer prognosis, cancer diagnosis, therapy failure, relapse, recurrence, and the like) based on genomic and proteomic signatures, gene expression, and pathways & networks activation of endogenous human stem cell-associated retroviruses (SCAR). This disclosure is also directed to methods of targeting, designing, and using treatments for clinically intractable malignant tumors.