Probabilistic hierarchical clustering for biological data

Biological data, such as gene expression profiles or protein sequences, is often organized in a hierarchy of classes, where the instances assigned to "nearby" classes in the tree are similar. Most approaches for constructing a hierarchy use simple local operations, that are very sensitive to noise or variation in the data. In this paper, we describe probabilistic abstraction hierarchies (PAH) [11], a general probabilistic framework for clustering data into a hierarchy, and show how it can be applied to a wide variety of biological data sets. In a PAH, each class is associated with a probabilistic generative model for the data in the class. The PAH clustering algorithm simultaneously optimizes three things: the assignment of data instances to clusters, the models associated with the clusters, and the structure of the abstraction hierarchy. A unique feature of the PAH approach is that it utilizes global optimization algorithms for the last two steps, substantially reducing the...

• Type: Conference
• Year: 2002
• Where: RECOMB
• Authors: Eran Segal, Daphne Koller
• Keywords: Computational Biology, Global Optimization Algorithms, HIV Protease Sequence, RECOMB 2002, Simple Local Operations