Learn More
The Drosha-DGCR8 complex initiates microRNA maturation by precise cleavage of the stem loops that are embedded in primary transcripts (pri-miRNAs). Here we propose a model for this process that is based upon evidence from both computational and biochemical analyses. A typical metazoan pri-miRNA consists of a stem of approximately 33 bp, with a terminal loop(More)
—DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods(More)
We consider the problem of learning a local metric to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic(More)
Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the(More)
Genetic algorithms have been used for neural networks in two main ways: to optimize the network architecture and to train the weights of a xed architecture. While most previous work focuses on only one of these two options, this paper investigates an alternative evolutionary approach called Breeder Genetic Programming (BGP) in which the architecture and the(More)
A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each n e t work is represented as a tree whose depth and width are dynamically adapted to the particular application by speciically deened genetic operators. The weights are trained by a(More)
This paper describes a method for an information filtering agent to learn user's preferences. The proposed method observes user's reactions to the filtered documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the most significant terms that best represent user's interests. In contrast to conventional(More)
Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene(More)
— Hypernetworks consist of a large number of hy-peredges that represent higher-order features sampled from training patterns. Evolutionary algorithms have been used as a method for evolving hypernetworks. The order of a hyperedge is defined as the number of feature variables in the hyperedge and it is an important parameter of the hypernetwork model.(More)