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.


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.

Dermatology Department Lab

The faculty and staff at the University of Missouri's Department of Dermatology are dedicated to providing high quality dermatological care for our patients, exceptional educational experiences for our residents and students, and the ongoing pursuit of new knowledge and scholarship. We provide dermatology care for patients at University of Missouri Health Care Hospital and Clinics, the Harry S. Truman Memorial Veterans' Hospital, and the Ellis Fischel Cancer Center. We also have been conducting teledermatology clinics in rural, underserved areas of Missouri for over 15 years.

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....

Zou Lab

We are developing computational methods to calculate binding free energies for ligand-receptor complexes. The derived energy models are applied to protein-substrate interactions, protein-protein interactions, and structure-based drug design. We are also developing new docking algorithms to account for protein flexibility. Methods used in our laboratory include computer modeling, simulation and graphics display. Additional application includes modeling of structure-function relationship of membrane proteins.

Chemical Ecology Lab

The Schultz-Appel Chemical Ecology Lab at the University of Missouri is directed by Jack Schultz and Heidi Appel of the Plant Sciences Division, College of Agriculture, Food and Natural Resources. Our lab addresses broad ecological and evolutionary questions in insect and plant science by using chemical, biochemical, and molecular methods.

MU Soybean Genomics Laboratory

The laboratory is directed by Dr. Henry T. Nguyen, Missouri Soybean Merchandising Council (MSMC) Endowed Professor of Genetics and Soybean Biotechnology. Research interests in the laboratory focus on the molecular genetics of plant stress tolerance and the application of genomics and genetic engineering technologies to soybean improvement.ethods.

National Center for Soybean Biotechnology

By conducting and providing research in soybean genomics and biotechnology, we contribute to the genetic improvement of soybeans for food, human health, and industrial uses, while increasing the profitability of the U.S. soybean industry.

Plant Root Genomics Consortium

Plant roots play a vital role in water and mineral acquisition, and are essential for plant growth and development. Under conditions of drought, roots can adapt to continue growth while at the same time producing and sending early warning signals to shoots which inhibit plant growth above ground. The plant root system, often referred to as "the hidden half," has received much less attention compared to the shoot. We have formed a Plant Root Genomics Consortium dedicated to root genetics and physiology. The aim of this consortium is to develop an understanding of the molecular mechanisms used by plant roots to acquire water and minerals from the soil, to elucidate the role roots play in adaptation to drought conditions, and to transfer this knowledge to crop improvement through biotechnology.

Tang Lab

Situated in the new Schweitzer Hall Addition, Department of Biochemistry at the University of Missouri - Columbia, the Tang laboratory utilizes state-of-the-art nuclear magnetic resonance or NMR and various biochemical/biophysical/computational techniques to characterize macromolecular structure and dynamics in solution.


The MUII core faculty received substantial competitive funding from the National Science Foundation (NSF), the National Institutes of Health (NIH), the National Institute of Justice (NIJ), US Army, Missouri Soybean Merchandising Council and Industries.

FacultySponserProject TitleAmountDuration
Shyu, Chi-Ren (PI)NY UniversityADRF and Education Course Development$70,00004/15/2019-
James Keller (Co-I)National Library of MedicineLiguistic summarization of Sensor Data for Early Illness Recogniation in Eldercare$911,98808/01/2016-
James Keller (PI)Office of Naval ResearchEnvironmentally aware feature extraction/selection and classification of underwater objects in synthetic apeture sonar imagery for mine countermeasures$864,64005/01/2016-
James Keller (PI)Army Research OfficeMulitipal and Multi Sensor Research for Explosive Hazard Detection$3,000,00405/01/2017-
Prasad Calyam (PI)National Science FoundationPFI-RP: Urban-Gradient Living Lab for Smart End-user App Delivery in ElderCare-as-a-Service$748,23402/01/2018-
Guilherme DeSouza (PI)National Science FoundationNRI: INT: Distributed Multi-Modal Robots for High-throughput Field Phenotyping$1,195,88002/01/2018-
Jianlian Cheng (PI)National Institutes for HealthMulti-Scale Modeling, Validation and Visualization of 3D Genome Structure$2,139,58502/01/2018-
Dr. Gregory Alexander (PI)AHRQA National Report of Quality Measures and Information Technology in Nursing Homes-Renewal$1,995,52209/30/2017-
Elizabeth King (PI)NSFQuantitative genetics of learning and memory in Drosophila$462,90006/15/2017-
Shi-Jie Chen (PI)NIHNew computational tools for predicting ion effects in RNA structures$1,219,72102/01/2017-
Prasad Calyam (Co-PI)NSFUS Ignite: A Networked Virtual Reality Platform for Online Social Learning of Youth with Autism Spectrum Disorders$599,16002/01/2017-
Prasad Calyam (PI)NSFREU SITE: Research in Consumer Networking Technologies$359,99902/01/2017-
Prasad Calyam (PI)NSFUS Ignite: Resilient Virtual Path Management for Scalable Data-intnesive Computing at Networ-Edges$203,81501/01/2017-
Jeffery Belden (Co-I)NIHOptimizing Display of Blood Pressure Data to Support Clinical Decision Making$2,272,27201/04/2015-
Jared Decker (PI)USDANational Animal Genome Research Program$50,00001/01/2013-
Jared Decker (PI)USDAValidation of a Multi-Breed Beef Tenderness Genomic Prediction Test using Retail Steaks$15,00001/01/2013-
Jianlin Cheng (PI)NIHIntegrated Prediction and Validation of Protein Structures$1,300,00001/08/2016-
Chi-Ren Shyu (PI)NIHT32: Massive and Complex Data Analysis Pre-Doctoral Training in One Health$1,453,55501/04/2016-


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Adhikari, B; Cheng, J

CONFOLD2: improved contact-driven ab initio protein structure modeling (Journal Article)

BMC Bioinformatics, 19 (1), pp. 22, 2018, ISSN: 1471-2105 (Electronic) 1471-2105 (Linking).

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Oluwadare, O; Zhang, Y; Cheng, J

A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data (Journal Article)

BMC Genomics, 19 (1), pp. 161, 2018, ISSN: 1471-2164 (Electronic) 1471-2164 (Linking).

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Adhikari, B; Hou, J; Cheng, J

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning (Journal Article)

Proteins, 86 Suppl 1 , pp. 84-96, 2018, ISSN: 1097-0134 (Electronic) 0887-3585 (Linking).

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Axtell, M J; Meyers, B C

Revisiting Criteria for Plant MicroRNA Annotation in the Era of Big Data (Journal Article)

Plant Cell, 30 (2), pp. 272-284, 2018, ISSN: 1532-298X (Electronic) 1040-4651 (Linking).

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Baldrich, P; Beric, A; Meyers, B C

Despacito: the slow evolutionary changes in plant microRNAs (Journal Article)

Curr Opin Plant Biol, 42 , pp. 16-22, 2018, ISSN: 1879-0356 (Electronic) 1369-5266 (Linking).

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Belanger, S; Marchand, S; Jacques, P E; Meyers, B; Belzile, F

Differential Expression Profiling of Microspores During the Early Stages of Isolated Microspore Culture Using the Responsive Barley Cultivar Gobernadora (Journal Article)

G3 (Bethesda), 2018, ISSN: 2160-1836 (Electronic) 2160-1836 (Linking).

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Spencer, M; Takahashi, N; Chakraborty, S; Miles, J; Shyu, C R

Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups (Journal Article)

J Biomed Inform, 77 , pp. 50-61, 2018, ISSN: 1532-0480 (Electronic) 1532-0464 (Linking).

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Linde, Van Der K; Timofejeva, L; Egger, R L; Ilau, B; Hammond, R; Teng, C; Meyers, B C; Doehlemann, G; Walbot, V

Pathogen Trojan horse delivers bioactive host protein to alter maize (Zea mays) anther cell behavior in situ (Journal Article)

Plant Cell, 2018, ISSN: 1532-298X (Electronic) 1040-4651 (Linking).

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Ma, W; Chen, C; Liu, Y; Zeng, M; Meyers, B C; Li, J; Xia, R

Coupling of microRNA-directed phased small interfering RNA generation from long noncoding genes with alternative splicing and alternative polyadenylation in small RNA-mediated gene silencing (Journal Article)

New Phytol, 217 (4), pp. 1535-1550, 2018, ISSN: 1469-8137 (Electronic) 0028-646X (Linking).

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Monsees, E; Goldman, J; Popejoy, L

Staff nurses as antimicrobial stewards: An integrative literature review (Journal Article)

Am J Infect Control, 45 (8), pp. 917-922, 2017, ISSN: 1527-3296 (Electronic) 0196-6553 (Linking).

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Xu, X; Duan, D; Chen, S J

CRISPR-Cas9 cleavage efficiency correlates strongly with target-sgRNA folding stability: from physical mechanism to off-target assessment (Journal Article)

Sci Rep, 7 (1), pp. 143, 2017, ISSN: 2045-2322 (Electronic) 2045-2322 (Linking).

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Edger, P P; Smith, R; McKain, M R; Cooley, A M; Vallejo-Marin, M; Yuan, Y; Bewick, A J; Ji, L; Platts, A E; Bowman, M J; Childs, K L; Washburn, J D; Schmitz, R J; Smith, G D; Pires, J C; Puzey, J R

Subgenome Dominance in an Interspecific Hybrid, Synthetic Allopolyploid, and a 140-Year-Old Naturally Established Neo-Allopolyploid Monkeyflower (Journal Article)

Plant Cell, 29 (9), pp. 2150-2167, 2017, ISSN: 1532-298X (Electronic) 1040-4651 (Linking).

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Xu, X; Qiu, L; Yan, C; Ma, Z; Grinter, S Z; Zou, X

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions (Journal Article)

Proteins, 85 (3), pp. 424-434, 2017, ISSN: 1097-0134 (Electronic) 0887-3585 (Linking).

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Fain, K M; Alexander, G C; Dore, D D; Segal, J B; Zullo, A R; Castillo-Salgado, C

Frequency and Predictors of Analgesic Prescribing in U.S. Nursing Home Residents with Persistent Pain (Journal Article)

J Am Geriatr Soc, 65 (2), pp. 286-293, 2017, ISSN: 1532-5415 (Electronic) 0002-8614 (Linking).

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Yang, Chang Xiao Fu Yabo. Duan Ye D

A Method to Accurately Detect Landmark Pairs in the Images to Support Evaluation of Deformable Image Registration Methods (Journal Article)

Medical Physics, 2017.

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Fain, K M; Castillo-Salgado, C; Dore, D D; Segal, J B; Zullo, A R; Alexander, G C

Inappropriate Fentanyl Prescribing Among Nursing Home Residents in the United States (Journal Article)

J Am Med Dir Assoc, 18 (2), pp. 138-144, 2017, ISSN: 1538-9375 (Electronic) 1525-8610 (Linking).

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Yao, Q; Xu, D

Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB (Journal Article)

Methods Mol Biol, 1558 , pp. 127-138, 2017, ISSN: 1940-6029 (Electronic) 1064-3745 (Linking).

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Feng, X Y; Liang, Y C; Shi, X H; Xu, D; Wang, X; Guan, R C

Overfitting Reduction of Text Classification Based on AdaBELM (Journal Article)

Entropy, 19 (7), 2017, ISSN: 1099-4300.

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Ng'oma, E; Perinchery, A M; King, E G

How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies (Journal Article)

Proceedings of the Royal Society B-Biological Sciences, 284 (1857), 2017, ISSN: 0962-8452.

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Yoo, I; Shehu, A

Guest Editorial for Special Section on BIBM 2014 (Journal Article)

Ieee-Acm Transactions on Computational Biology and Bioinformatics, 14 (2), pp. 252-253, 2017, ISSN: 1545-5963.

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821 entries « 1 of 42 »