Abstract: According to various embodiments, there is provided a method for identifying clusters in a dataset, the method including: determining for each data point in the dataset, a plurality of parameters including a first parameter and a second parameter, the first parameter being a distance between the data point and a nearest other data point having a local density that is higher than a local density of the data point, and the second parameter being a function of the local density of the data point and the first parameter; running statistical tests on each of the first parameter and the second parameter across the dataset, to identify outliers of the first parameter and outliers of the second parameter; and designating each data point where both the first parameter and the second parameter are identified outliers, as a centre of a respective cluster.

Abstract: A method of automatically assigning class labels to objects is provided. The method uses object data indicative of a plurality of parameters associated with each object. The method comprises (i) identifying, from the object data or from a lower-dimensional encoding of the object data a plurality of cluster centres in a d-dimensional space, each cluster centre corresponding to one of the class labels; (ii) for respective cluster centres, determining a surrounding region based on a nearest neighbour cluster centre, and assigning the respective class label to objects within the surrounding region; (iii) generating a predictive model using the object data, or the lower-dimensional encoding of the object data and the class labels of the assigned objects; and (iv) assigning class labels to unassigned objects using the predictive model. A corresponding system for performing the above method is also provided.

Abstract: The present invention provides a method for predicting prognosis of hepatocellular carcinoma patients based on measurement of the relative level of expression of a combination of 15 immune genes of interest, or a subset thereof, in the tumors of such patients. Tumor material can come from surgical resection or biopsy. The relative gene expression information may be combined in an algorithm. The signature can be used by itself or in combination with other information such as stage information.