The present invention relates to methods for the identification of cell culture factors for cell maintenance and cell conversion

The present invention relates to methods for the identification of cell culture factors for cell maintenance and cell conversion

Methods and systems for deconvoluting tumor ecosystems for personalized cancer therapy are disclosed. Generally, human cancers exhibit large variation in behavior between and within patients, which is in large part related to cellular composition. Identifying cell types can identify specific types of tumors and/or cancers present in an individual. Further embodiments generally describe identifying therapies from clinical trials to which the tumor or cancer ecotypes respond, thus providing personalized therapies based on the identified cancer or tumor type.

Methods and systems for deconvoluting tumor ecosystems for personalized cancer therapy are disclosed. Generally, human cancers exhibit large variation in behavior between and within patients, which is in large part related to cellular composition. Identifying cell types can identify specific types of tumors and/or cancers present in an individual. Further embodiments generally describe identifying therapies from clinical trials to which the tumor or cancer ecotypes respond, thus providing personalized therapies based on the identified cancer or tumor type.

A method of progressively training a base caller is disclosed. The method includes initially training a base caller, and generating labelled training data using the initially trained base caller; and (i) further training the base caller with analyte comprising organism base sequences, and generating labelled training data using the further trained base caller. The method includes iteratively further training the base caller by repeating step (i) for N iterations, which includes further training the base caller for N1 iterations of the N iterations with analyte comprising a first organism base sequence, and further training the base caller for N2 iterations of the N iterations with analyte comprising a second organism base sequence. A complexity of neural network configurations loaded in the base caller monotonically increases with the N iterations, and labelled training data generated during an iteration is used to train the base caller during an immediate subsequent iteration.

The present invention provides a novel method for the classification of adjuvants. In one embodiment, the present invention provides a method for generating organ transcriptome profiles for adjuvants, said method comprising: (A) a step for obtaining expression data by performing transcriptome analysis for at least one organ of a target organism by using at least two adjuvants; (B) a step for clustering the adjuvants with respect to the expression data; and (C) a step for generating the organ transcriptome profile for the adjuvants on the basis of the clustering.

Methods for diagnosis of sepsis are disclosed. In particular, the invention relates to the use of bio -markers for aiding diagnosis, prognosis, and treatment of sepsis, and to a panel of biomarkers that can be used to distinguish sepsis from noninfectious sources of inflammation, such as caused by traumatic injury, surgery, autoimmune disease, thrombosis, or systemic inflammatory response syndrome (SIRS).

Methods for diagnosis of sepsis are disclosed. In particular, the invention relates to the use of bio -markers for aiding diagnosis, prognosis, and treatment of sepsis, and to a panel of biomarkers that can be used to distinguish sepsis from noninfectious sources of inflammation, such as caused by traumatic injury, surgery, autoimmune disease, thrombosis, or systemic inflammatory response syndrome (SIRS).

The present invention generally relates to a method of diagnosing, prognosing and treating prostate cancer patients. Particularly, the present invention relates to a method of selecting patients with castration resistant prostate cancer (CrPC) for combination therapy comprising a selective dipeptidyl peptidase inhibitor and a PD-1 axis antagonist. The present invention provides a computational approach to identifying potential patients for CrPC. The present invention also relates to a method of treating castration resistant prostate cancer (CrPC) patients with said combination therapy.

The present invention generally relates to a method of diagnosing, prognosing and treating prostate cancer patients. Particularly, the present invention relates to a method of selecting patients with castration resistant prostate cancer (CrPC) for combination therapy comprising a selective dipeptidyl peptidase inhibitor and a PD-1 axis antagonist. The present invention provides a computational approach to identifying potential patients for CrPC. The present invention also relates to a method of treating castration resistant prostate cancer (CrPC) patients with said combination therapy.