ARTMAP-IC and Medical Diagnosis: Instance Counting and Inconsistent Cases

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking algorithm (MT+), the new algorithm (MT-) better approximates the real-time network differential equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pima Indian diabetes, breast cancer, heart disease, and gall bladder removal. ARTMAP-IC results arc equal to or better than those of logistic regression, K nearest neighbor (KNN), the ADAP perceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP dynamics are fast, stable, and scalable. A voting strategy improves prediction by training the system several times on different orderings of an input set. Voting, instance counting, and distributed representations combine to form confidence estimates for competing predictions.National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-95-J-0409, N00014-95-0657

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0175

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

COMBS: A Biosurveillance Ecosystem (BSVE) Prototype

The goal of DTRA's Biosurveillance Ecosystem (BSVE) program is to significantly reduce the time required to identify threats to human health, whether of malicious or natural origin, and respond appropriately. Draper Laboratory is leading a team to develop the Collaborative Overarching Multi-feed Biosurveillance System (COMBS) for BSVE to revolutionize biosurveillance capabilities.  USG analysts whose jobs are to reduce risks to national security will benefit from rapid and thorough information access, as will local public health authorities and individual citizens. This poster will provide an overview of the COMBS project and current progress towards meeting the goals of the BSVE

Mitochondrial Pseudogenes Suggest Repeated Inter-Species Hybridization among Direct Human Ancestors

The hypothesis that the evolution of humans involves hybridization between diverged species has been actively debated in recent years. We present the following novel evidence in support of this hypothesis: the analysis of nuclear pseudogenes of mtDNA (“NUMTs”). NUMTs are considered “mtDNA fossils” as they preserve sequences of ancient mtDNA and thus carry unique information about ancestral populations. Our comparison of a NUMT sequence shared by humans, chimpanzees, and gorillas with their mtDNAs implies that, around the time of divergence between humans and chimpanzees, our evolutionary history involved the interbreeding of individuals whose mtDNA had diverged as much as ~4.5 Myr prior. This large divergence suggests a distant interspecies hybridization. Additionally, analysis of two other NUMTs suggests that such events occur repeatedly. Our findings suggest a complex pattern of speciation in primate/human ancestors and provide one potential explanation for the mosaic nature of fossil morphology found at the emergence of the hominin lineage. A preliminary version of this manuscript was uploaded to the preprint server BioRxiv in 2017 (10.1101/134502)

Mitochondrial Pseudogenes Suggest Repeated Inter-Species Hybridization among Direct Human Ancestors

The hypothesis that the evolution of humans involves hybridization between diverged species has been actively debated in recent years. We present the following novel evidence in support of this hypothesis: the analysis of nuclear pseudogenes of mtDNA (“NUMTs”). NUMTs are considered “mtDNA fossils” as they preserve sequences of ancient mtDNA and thus carry unique information about ancestral populations. Our comparison of a NUMT sequence shared by humans, chimpanzees, and gorillas with their mtDNAs implies that, around the time of divergence between humans and chimpanzees, our evolutionary history involved the interbreeding of individuals whose mtDNA had diverged as much as ~4.5 Myr prior. This large divergence suggests a distant interspecies hybridization. Additionally, analysis of two other NUMTs suggests that such events occur repeatedly. Our findings suggest a complex pattern of speciation in primate/human ancestors and provide one potential explanation for the mosaic nature of fossil morphology found at the emergence of the hominin lineage. A preliminary version of this manuscript was uploaded to the preprint server BioRxiv in 2017 (10.1101/134502)