Abstract: A method of identifying a subject having Kawasaki disease (KD), which includes discriminating the subject from a subject having another condition, for example other infectious and inflammatory conditions, such as those that present similar symptoms to KD. Also provided is a minimal gene signature employed in the method, as well as primers, probes and gene chips for use in the method.

Abstract: The invention relates to a method for the diagnosis of TB in a subject, the method comprising (a) providing a sample from said subject, said sample being selected from the group consisting of: blood, serum and plasma; (b) determining the concentration in said sample of the following biomarkers: IL-1ra, IL6, IL-7, IL-8, IL-12p70, FGF-basic, IP-10, and VEGF; (c) converting each biomarker concentration determined in (b) into a decile value; and (d) converting each decile value into a binary presence or absence by comparing the decile values of (c) to the following specific quantile cut off values wherein a decile value matching or exceeding the specific quantile cut-off value is converted into the binary presence of the biomarker, and a decile value lower than the specific quantile cut-off value is converted into the binary absence of the biomarker; wherein detecting the presence of each of said biomarkers indicates that the subject has TB. The invention also relates to uses, kits and devices.

Abstract: In many situations it is required to predict if and/or when an event will occur after a trigger. For example, businesses such as banks would like to predict if and when their customers are likely to leave after a particular event such as closing a loan. The business is then able to take action to prevent loss of customers. Customer data including data about customer who have closed a loan and then left a bank for example, is used to create a Bayesian statistical model. A plurality of attributes are available for each customer and the model involves partitioning these attributes into a plurality of partitions. In one embodiment the Bayesian statistical model is a survival analysis type model and in another embodiment the model comprises fitting a Weibull distribution to the data in each of the partitions. The marginal likelihood of the data is calculated and then the method involves mixing over all possible partitions in a Bayesian framework.