Funded by AHRQ, NIH, NSF, USDA, and industry, MUII core faculty and trainees actively conduct cutting-edge research in bioinformatics, health informatics, biomedical informatics, and geoinformatics. Trainees have opportunities to rotate to both dry and wet labs in computing, information science, plant & animal genomics, and clinical departments. Many of the plant genomics and phenomics research projects are in collaboration with the MU Interdisciplinary Plants Group (IPG), while healthcare related topics are coordinated with the School of Medicine, School of Nursing, School of Health Professions, and the Tiger Institute. MU is also in a unique position to tackle Big Data challenges in one health through fostering collaborations among Engineering, Vet Medicine, and Medicine to sponsor 18 doctoral students through the BD2K T32 training program.  To explore the specific types of extramural funding and the directors of projects, please visit the “Grants” list.

Labs

Bioinformatics, Data Mining and Machine Learning lab (BDM)

The research in BDM lab is focused on developing machine learning and data mining methods to analyze big biomedical data for solving fundamental problems in biomedical sciences. Currently, we are developing bioinformatics algorithms and tools for protein structure and function prediction, 3D genomics, biological network modeling, and omics data analysis. The research is funded by both National Institutes of Health (NIH_ and National Science Foundation (NSF).

Data Science Lab

 Our lab develops powerful statistical and computational methods for the analysis of large and complex datasets, longitudinal data, survival data, and applied them to public health, medicine and social sciences, among others. In addition, we collaborate and consult with epidemiologists and clinicians on study design, data analysis and results interpretation.

Digital Biology Laboratory

The research focus of Digital Biology Laboratory (DBL) is Bioinformatics and Computational Biology. The lab works on development of novel computational methods, algorithms, software and information systems, as well as on broad applications of these tools and other informatics resources for various biological and medical problems. Research topics include application of deep learning in biological and medical data analysis and prediction, protein structure prediction, high-throughput biological data analysis, protein post-translation modification analysis, and computational biology studies of plants, cancers, and microbes. The lab has collaborated with dozens of research labs. 

Interdisciplinary Data Analytics and Search (iDAS) Lab

The iDAS Lab hosts trainees and researchers in computational science and informatics to design algorithms and develop methods for faster and more accurate predicative analytics and information search through large-scale information sources in biomedicine, engineering, and plant sciences with storytelling values for actionable decisions. Building upon rich research foundations, the lab has four active research directions in exploratory mining, Big Data search, visual knowledge retrievals, and block chains.  All theoretical works developed in the iDAS lab are required to be tested on real-world data sets, such as HealthFacts^® (60 million patients), National Impatient Sample (NIS), TCGA data, PDB, Simons Foundation Autism Research Initiative data, etc.

Pires Lab

Research in our lab broadly encompasses plant evolutionary biology- from phylogenetic studies in plant diversity to genome-wide analyses of gene expression. Current investigations are directed at molecular systematics and comparative genomics, with a particular focus on the evolution and ecology of polyploid plants. 

RNA Computational Biology Lab

Dr. Chen's lab has been developing computational methods for RNA structure and function and applications of the computational models to therapeutic designs. A main research method used in the lab is to design and implement knowledge-based database and to extract structure and function information from the database.  Examples of the current research efforts include RNA 3D structure prediction based on rules derived from the known structure database, prediction of metal ion binding sites on nucleic acids through data training, sgRNA design and genome-wide off-target prediction for CRISPR-Cas9 gene editing system, and RNA-based drug design.

The Decker Computational Genomics Group

The Decker Computational Genomics Group applies evolutionary biology to domesticated cattle, and other species as collaborations arise. We use the history of cattle populations to create genomic selection tools to improve cattle. Working at the intersection of two disciplines (bovine genomics and evolutionary biology) allows us to access large datasets, often produced for other research or commercial purposes. Our research program also has a transnational component in which we take basic research and transform it into decision support tools for farmers and ranchers.

The King Lab

In the King Lab, we are trying to understand the genetic basis and evolution of organismal allocation patterns. The core life processes for every organism, such as surviving in the environment, finding food and mates, and reproducing, require the organism to allocate some of its limited resources to these functions. Different selective pressures have produced the diversity of strategies that we see within and among species in how and when to allocate resources to different structures and functions. We use both computational and empirical techniques to try to understand both how different allocation strategies evolve and the underlying genetic architecture of this highly complex trait.

The Meyers Lab

Dr. Meyers's current research includes programs that emphasize bioinformatics and plant functional genomics. These programs include (1) analyses of small RNA, DNA methylation and the genomes of rice, Arabidopsis and other species using short-read DNA sequencing technologies, (2) development and implementation of novel informatics approaches for the storage, analysis, display, and public release of these data, (3) functional and evolutionary analyses of several gene families of interest, particularly miRNAs, phasiRNAs and the proteins involved in their biogenesis, as well as NB-LRR disease resistance genes.

Translational & Cancer Bioinformatics Laboratory

The TCBI lab conducts research in translational and cancer bioinformatics, imaging informatics, pathology informatics, computational epigenetics, and precision medicine informatics under the leadership of Dr. Dmitriy Shin. We draw from the experience of leading pathologists to study computational reasoning over molecular knowledge networks, function of long non-coding RNAs, molecular underpinnings of intra-tumor heterogeneity, brain damage imaging, diagnostics heuristics, pathway analysis, knowledge representation and knowledge complexity reduction, and many other topics. The lab is part of Pathology Informatics, a division of Pathology and Anatomical Sciences Department, and MU Informatics Institute.

Virtualization, Multimedia and Networking Lab (VIMAN)

At VIMAN Lab, we investigate novel methods to model, measure, manage and secure distributed computing applications that have real-time resource allocation needs and require high-speed networks. With broadband access and cloud computing becoming integral in our society, end-user applications (e.g., video-on-demand, videoconferencing, remote visualization, remote instrumentation, real-time data analytics) are increasingly becoming data-intensive, mobility-supported and network-dependent. Consequently, we are motivated by the challenges in studying theoretical foundations as well as developing tools for networks and cloud platforms. We strive for meeting the end-user Quality of Experience (QoE) expectations in applications (e.g., healthcare, advanced manufacturing, public safety, new media, bioinformatics).

Vision-Guided and Intelligent Robotics Lab (ViGIR)

In the ViGIR lab, we conduct research in areas such as: Computer Vision, Pattern Recognition and Robotics. We develop models to represent the world as perceived by cameras and other sensors and we devise algorithms that make use of such models to extract real time information from a sensory network in order to guide robots to perform various tasks. Our main goal is to build new Human-Robot Interfaces that can be used in robotic assistive technology, augmented reality, automation, tele-operation, etc....