Abstract: A method for image-based hepatocellular carcinoma (HCC) molecular subtype classification may include determining, within an image depicting a plurality of cells of a biological sample, a plurality of tiles with each tile depicting a portion of the plurality of cells comprising the sample. A machine learning model may be applied to determine a molecular subtype for the portion of the plurality of cells depicted in each tile. Moreover, an overall molecular subtype for the plurality of cells depicted in the image of the biological sample may be determined based on the molecular subtype of the portion of the plurality of cells depicted in each tile of the plurality of tiles. For example, another machine learning model may be applied to determine the overall molecular subtype of the plurality of cells depicted in the image of the biological sample. Related systems and computer program products are also provided.

Abstract: Histologic classification of pathology specimens through machine learning is a nascent field which offers tremendous potential to improve cancer medicine. Its utility has been limited, in part because of differences in tissue preparation and the relative paucity of well-annotated images. We introduce tissue recognition, an unsupervised learning problem analogous to human face recognition, in which the goal is to identify individual tumors using a learned set of histologic features. This feature set is the “tissue fingerprint.” Because only specimen identities are matched to fingerprints, constructing an algorithm for producing them is a self-learning task that does not need image metadata annotations. Here, we provide an algorithm for self-learning tissue fingerprints, that, in conjunction with color normalization, can match hematoxylin and eosin stained tissues to one of 104 patients with 93% accuracy.

Abstract: A number of methods and computer systems related to mass spectrometric data analysis are disclosed. Adoption of the disclosure herein facilitates automated, high throughput, rapid analysis of complex datasets such as datasets generated through mass spectrometric analysis, so as to reduce or eliminate the need for oversight in the analysis process while rapidly yielding accurate results.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.