Genetic targets for autism spectrum disorder identified by MU team
February 22, 2018
COLUMBIA, Mo. – Autism is a spectrum of closely related symptoms involving behavioral, social and cognitive deficits. Early detection of autism in children is key to producing the best outcomes; however, searching for the genetic causes of autism is complicated by various symptoms found within the spectrum. Now, a multi-disciplinary team of researchers at the […]
An RNAmazing research breakthrough
December 19, 2017
Professor of Bioengineering and the Dalton Cardiovascular Research Center Li-Qun (Andrew) Gu and Shi-Jie Chen, joint Professor of Physics, Biochemistry and the MU Informatics Institute and their team recently published “Nanopore electric snapshots of an RNA tertiary folding pathway,” in the prestigious journal Nature Communications.
Dr. Gregory Alexander recently awarded AHRQ grant.
October 9, 2017
Dr. Gregory Alexander, from The Sinclair School of Nursing, was recently awarded a $1,995,522.00 AHRQ grant. This grant will support an interdisciplinary research team who are already contributing to clinical research in long-term care settings. The PI is a doctorally-prepared RN and fellow in the American Academy of Nursing with over two decades of work […]
240 Naka Hall
PROTEIN TRANSPORT: BIOINFORMATICS METHODS FOR UNDERSTANDING PROTEIN SUBCELLULAR LOCALIZATION
Eukaryotic cells contain diverse subcellular organelles. These organelles form distinct
functional cellular compartments where different biological processes and functions are
carried out. The accurate translocation of a protein is crucial to establish and maintain
cellular organization and function. Newly synthesized proteins are transported to different
cellular components with the assistance of protein transport machineries and complex
targeting signals. Mis-localization of proteins is often associated with metabolic disorders
and diseases. Compared with experimental methods, computational prediction of protein
localization, utilizing different machine learning methods, provides an efficient and
effective way for studying the protein subcellular localization on the whole-proteome level.
Here, we present in this dissertation the bioinformatics methods for studying protein
subcellular localization. We reviewed the studies of protein subcellular transport and
machine learning methods in bioinformatics, presented our work on mitochondrial protein
targeting prediction in plants, summarized the ongoing development of a web-resource for
protein subcellular localization, and discussed the future work and development.
M212 Medical Science Building
Informatics framework for the identification of diagnostic discrepancies and errors
103 Animal Science Research Center