Abstract: An algorithm presented in a suitable set of software code for the identification of relationships among data points or data sets when the data points or data sets constitute multivariate responses and a plurality of features of interest from a plurality of biological tissue samples

Abstract: Thread-based methods and systems for using the idle processing power of one or more networked computers include at least one server and at least one client. The server stores data and job descriptions relating to a complex scientific problem and provides the job descriptions and portions of the data to clients in response to requests from the clients. Each of the clients starts a first idle thread for pulling job descriptions from the server. Each client also starts a second idle thread for requesting data from the server and for performing the job specified in the job description. The first and second idle threads are automatically scheduled for execution by the operating system.

Abstract: Conditional associations among features in samples are identified by defining a matrix having rows that represent the samples and columns that represent the features. Each row-column position of the matrix has a first binary value if the sample that is associated with the row exhibits the feature that is associated with the column, and a second binary value if a sample that is associated with the row does not exhibit the feature that is associated with the column. Recursive partitioning then is performed for each column. In particular, for each column, the column is recursively partitioned relative to the remaining ones of the columns, to define a tree of conditional branches for the rows for each column. The collection of trees of conditional branches for the columns may be displayed and/or analyzed to identify conditional associations of interest. Continuous features, wherein each row-column position of the matrix has a value selected from a continuous range of values, also may be analyzed.