The IUPUI Signature Center on Bio-Computing

poster abstractBio-Computing is the discipline that integrates biomedical concepts and Computer Science techniques for collecting, managing, processing and analyzing large-scale biomedical data, as well as enables a deeper understanding of biological processes and medical procedures through modeling, simulation, and visualization. Bio-Computing emphasizes the algorithmic, computational, and software system issues arising from biomedical problems. It focuses on developing new, improved, specialized and customized Computer Science techniques and tools for computing related needs in life science applications that do not have ready-to-use solutions. The IUPUI Signature Center on Bio-Computing (SCBC) aims to act as a catalyst to provide BioComputing infrastructure and expertise for Indiana life science initiative. The specific mission is the following: • Bio-Computing Infrastructure: To develop cutting-edge bio-computing techniques and tools to establish an infrastructure as a framework to support life science applications. • Collaborative Projects: To actively engage in collaborative research projects, and maximize the impact of bio-computing in life science research and funding efforts. The scope of the projects supported by SCBC can be best described by the figure below

CIVILITY: Cloud based interactive visualization of tractography brain connectome

Cloud based Interactive Visualization of Tractography Brain Connectome (CIVILITY) is an interactive visualization tool of brain connectome in the cloud. This application submits tasks to remote computing grids were the CIVILITY-tractography pipeline is deployed. The application will list the running tasks for the user and once a task is completed the brain connectome is visualized using Hierarchical Edge Bundling. The analysis pipeline uses FSL tools (bedpostx and probtrackx2) to generate a triangular matrix indicating the connectivity strength between different regions in the brain. This work is motivated by medical applications in which expensive computational tasks such as brain connectivity is needed and to provide a state of the art visualization tool of Brain Connectome. This work does not contribute any novelty with respect to the visualization methodology, is rather a new resource for the neuroimaging community. This work is submitted to the SPIE Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho

A brief review of surface meshing in medical images for biomedical computing and visualization

A visual representation of the interior of a body is important for clinical analysis and medical intervention. The technique, process and art of creating this visual representation are called medical imaging. The images produced from medical imaging need to be analyses by using Finite Element Method (FEM) especially for intraoperative registration and biomechanical modeling of the tissues. This medical model ranges from the smallest vascular to bones and the complex brain. In order to use FEM, the images need to go through surface meshing generator. Although numerous mesh generation methods have been described to date, there is a few which can deal with medical data input. In this paper, a briefing review of surface meshing that can deal in medical images is presented especially in biomedical computing and visualization. Some automatic mesh generators software used in medical imaging is also discussed such as ScanIP, MIMICS, TETGEN, NetGen, BioMesh3D,CUBITMesh and Gmsh

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others