William F. Albright Papers

This file contains a finding aid for the William F. Albright Collection. To access the collection, please contact the archivist (asorarch@bu.edu) at the American Schools of Oriental Research, located at Boston University.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.