A neural network classification method has been developed as an alternative approach to the search/organization problem of large modular databases. Two artificial neural systems have been implemented on a Cray for rapid protein/nucleic acid classification of unknown sequences. The system employs a n-gram hashing function for sequence encoding and modular backpropagation networks for classification. The protein system has achieved a 82 to 100% sensitivity at a speed that is about an order of magnitude faster than other search methods. With the rapid accumulation of sequences, the saving in time will become increasingly significant. The pilot nucleic acid system showed a 96% classification accuracy. The software tool would be valuable for the organization of molecular sequence databases and is generally applicable to any databases that are organized according to family relationships.>

An approach to the prediction of protein structure/function from primary sequence that is a direct protein/domain classification using a neural network is presented. The protein classification system, PROCANS, is able to classify unknown proteins to protein/domain classes by imbedding a domain database in a back-propagation network. The initial results of using PROCANS for 30 training records are discussed. The system converged in 89 Cray CPU seconds during training, and the classification was rapid and accurate for all unknown cases tested. The algorithm differs from many other methods in that it does not require an exhaustive database search or a primary sequence alignment for sequence comparisons. These features make it possible to scale-up the system to embed the entire domain database and classify at least 1000 protein/domain classes

Artificial Intelligence programming involves representing knowledge, using paradigms to manipulate the knowledge, and having a learning process modify both the knowledge and the paradigms. One could consider this process as building a model of how one thinks, i.e. how the brain operates at the cognitive psychology level [2]. Recently, cognitive scientists have developed a model of how one thinks at the neural level. This model is called the Parallel Distributed Processing (PDP) model of cognition and is described in the definitive work of Rumelhart and McClelland [1]. The idea that we can actually model the brain as an electrical network of neurons and then develop Artificial Intelligence in terms of the model is extremely attractive. The program has had some success, especially in the area of sensory perception and motor activity, but still has some problems to overcome before it can be said to be the ideal foundation for Artificial Intelligence.

This paper outlines a set of workshops to provide training for certified high school computer science teachers. Upon the completion of the four core workshops, a high school teacher would have an excellent background to teach high school computer science as detailed in the new proposed ACM Curriculum for high school certification. The workshops should also do a good job upgrading the background of high school computer science and computer math teachers to teach courses currently in the high school curriculum. One workshop, PASCAL with Applications to Data Structures, was specifically designed to prepare current high school teachers to teach a PASCAL course whose goal is to prepare students for the advanced placement test. Each of the six workshops is a three semester hour course and most carry graduate credit. Three of the six courses have already been offered and more should be taught next summer. The reception of the high school teachers to the workshops has been very enthusiastic.