Further knowledge in health through computation, data visualization and analysis

Our group works at the interface of computer science, biology, and mathematics by applying computational approaches to the seas of data in biomedical research. One of the main interests of our group is the development of technologies to make large-scale computational approaches accessible and more collaborative to a wider scientific audience. Our recent web-based technology, Cell Collective, enables scientists from across the globe to construct and simulate large-scale computational models of biological systems in a highly collaborative fashion. This software enables biomedical researchers to study the dynamics of biological systems (e.g., cells) under both healthy and diseased conditions. Cell Collective provides a unique environment for real-time, interactive simulations to enable users to analyze and visualize the multitude of effects a disease-related malfunction can have on the rest of the cell. Over the last couple of years, Cell Collective has also made its way into classrooms, where students in life sciences courses can learn about biological processes by building, simulating, breaking, and re-building computational models of these processes. Cell Collective now supports about 2,000 students/year in introductory life sciences courses in 10+ universities.

Other technologies developed by our organization include cost-effective mobile disease monitoring devices, interactive on-line tissue sample analysis, an interactive statistical analysis platform for teaching life sciences students about data analysis, etc.

Our group consists of computer scientists, biochemists, biologists, bioinformaticians, as well as mathematicians, creating an unique environment of diverse skills, integrated by a single interest point.

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  • javascript
  • webgl
  • reactjs
  • flask


  • Science and Medicine
  • biological networks
  • bioinformatics
  • network simulation
  • computational biology
  • omics data
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Computational Biology @ University of Nebraska-Lincoln 2018 Projects

  • Rupav Jain
    Candis: A Software Tool for Cancer Prediction And Biomarker Identification Using High-throughput Data
    Candis (portmanteau of Cancer and Discover) is an Open Source data mining suite (released under the GNU General Public License v3) for DNA...
  • Gaurav Grover
    ccNetViz is a high-performance graph data visualization library that runs on WebGL (an in-browser library to run 3D graphics on GPU parallel...
  • Tejasav Khattar
    Interactive Web Platform for R based Data Analysis
    The goal of this project is to develop the final version of cross-platform web-based application that enables anyone to perform various statistical...
  • Ana Jeličić
    Javascript/WebGL Library For Interactive Visualization Of Large-Scale Network Graphs: Expanded Features.
    In this project I will add some new options to the CCNetViz JavaScript library (e.g. adding hide/show for nodes/edges, adding the context menu on...