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 Projects

 Computational Models of Multisensory Processing

Although multisensory processing is a ubiquitous sensory effect, computational models of the phenomenon have primarily used quantitative mathematical techniques largely to simulate multisensory enhancement. On the other hand, biologically-close neuron models and networks are extensively used in other fields of computational neuroscience to simulate a broad array of neuronal processing. The goal of this study is use a network of spiking neurons with synaptic plasticity rules to model the full range of multisensory integration (enhancement, depression and subthreshold).

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 Chromosome 21 gene function and pathway database

Down syndrome is the most common genetic cause of intellectual disability. The complete phenotype is complex and variable among individuals. It is due to an extra copy of all or part of a normal chromosome 21 and the increased expression, due to gene dosage, of the normal genes encoded by it. The large number of genes involved (>300), their functions and interactions, the perturbations of cellular pathways and processes their increased expression causes, are a challenge to the determination of gene-phenotype correlations.   This database is designed as a tool for researchers interested in individual chromosome 21 genes, groups of genes, their orthologues in models organisms, as well as Down syndrome and mouse models of Down syndrome. The goal of the database is to:

  • provide curated, annotated comprehensive information on chromosome 21 genes
  • reduce redundant efforts in database and literature searches
  • provide links to primary data sources for user evaluation
  •  The data have been compiled from public sequence and genomic analysis databases (NCBI, ydb, wormbase, flybase, protein interaction databases) and from the literature. Expert review and curation of the data are ongoing. Corrections and updated information from users are appreciated.
    Chromosome 21 gene function and pathway database

     Diagnosing Coronary Artery Disease from Bull's Eye SPECT Images

    The goal of this project is to come up with machine-learning generated diagnostic rules to distinguish between normal and abnormal patients based on Bull's eye SPECT imaging of the heart. Abnormal patients are those who were found (in our case) to have either single coronary artery disease (SVD) or double vessel disease (DVD), using coronary arteriography. We have a database containing 160 NORMAL (86 males + 75 females) SPECT images in two views: stress and delayed, and and 24 ABNORMAL (19 males + 4 females) images, also in stress and delayed views.  This quite unique (a large number of normal patients) data set was collected by Dr. Cios at the National Institute of Cardiology in Warsaw. To achieve our goal we have created a template and superimposed it on the original Bull's eye SPECT images to indicate boundaries of the coronary vascular territories, namely, left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA). Next, we extracted the regions of interest corresponding to LAD, RCA and LCX from the Bull's eye quantitative polar map and discretized it. Finally, the CLIP3 machine learning algorithm was used to come up with diagnostic rules.