March 7, 2018

Seasonal Influenza Vaccine: Not easy shot to get

During the past nearly 50 years, antigenic variants of subtype H3N2 influenza A viruses have frequently emerged, causing significant public health challenges. The manner in which these variants emerge and their patterns of spread are not well understood. We identified 15 antigenic drift events with 16 antigenic variants during 1968–2016 by using a novel genomic sequence–based antigenicity inference method on ~40,000 H3N2 viruses. New antigenic variants were shown to emerge from certain locations in other continents rather than from Asia alone, and variants emerged year-round and took <2 months to spread across multiple continents. The uncertainty of the location of…

Feb. 23, 2018

REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics

Pathway-based analysis holds promise to be instrumental in precision and personalized medicine analytics. However, the majority of pathway-based analysis methods utilize “fixed” or “rigid” data sets that limit their ability to account for complex biological inter-dependencies. Here, we present REDESIGN: RDF-based Differential Signaling Pathway informatics framework. The distinctive feature of the REDESIGN is that it is designed to run on “flexible” ontology-enabled data sets of curated signal transduction pathway maps to uncover high explanatory differential pathway mechanisms on gene-to-gene level. The experiments on two morphoproteomic cases demonstrated REDESIGN’s capability to generate actionable hypotheses in precision/personalized medicine analytics.

Feb. 22, 2018

Genetic targets for autism spectrum disorder identified by MU team

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 University of Missouri created a new computational method that has connected several target genes to autism. Recent discoveries could lead to screening tools for young children and could help doctors determine correct interventions when diagnosing autism. Unlocking the genetic causes of autism requires data-intensive computations.

Feb. 8, 2018

MU-LOC: A Deep Neural Network Method for Predicting Mitochondrially Localized Proteins in Plants

Targeting and translocation of proteins to the appropriate subcellular compartments is crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale…

Jan. 31, 2018

An Analysis of Diabetes Mobile Applications Features Compared to AADE7TM: Addressing Self-Management Behaviors in People with Diabetes

Diabetes Self-management (DSM) applications (apps) have been designed to improve knowledge of diabetes and self-management behaviors. However, few studies have systematically examined if diabetes apps followed the American Association of Diabetes Educators (AADE) Self-Care BehaviorsTM guidelines. The purpose of this study was to compare the features of current DSM apps to the AADE7TM guidelines. In two major app stores, we used three search terms to capture a wide range of diabetes apps. Apps were excluded based on five exclusion criteria. A multidisciplinary team analyzed and classified the features of each app based on the AADE7TM. We conducted interviews with six…

Jan. 28, 2018

Collaborations across disciplines: MU Thyroid Nodule Electronic Database (MU-TNED), a multidisciplinary informatics approach

Thyroid nodules are common findings and thyroid cancer is projected to be one of the leading causes of cancer in women. The EHR includes the necessary data needed to connect clinical research with patient outcomes. The objective for this project was to develop and validate a usable informatics tool for clinicians and researchers to record, analyze, and be able to manipulate the clinical and research data to benefit all collaborators. The tool was specifically designed to enable follow-up in a longitudinal manner to support multiple aspects of research. The informatics tool MU-TNED was designed with a multidisciplinary team including the…

Dec. 19, 2017

An RNAmazing research breakthrough

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.

Dec. 5, 2017

MODELING THE HIPPOCAMUS: FINELY CONTROLLED MEMORY STORAGE USING SPIKING NEURONS

The hippocampus, an area in the temporal lobe of the mammalian brain, participates in the storage of personal memories and life events, including traumatic memories and the consequent symptoms of post-traumatic stress, giving importance to the study of the machinery of hippocampal memory storage and retrieval. The circuit is known to be controlled by the neuromodulator Acetylcholine, which switches the circuit between the memory storage state and the memory retrieval state. We built a computational model of the hippocampus with the ability to perform both memory storage and retrieval functions, controlled by the level of Acetylcholine. This functional separation decrease…

Dec. 1, 2017

Use of the N-ary Relational Schema to Atomize Compound Relational Triples

Electronic medical records document health information in structured format and in unstructured free text format.  Health information in structured format contains laboratory results, vital signs, patient demographics etc.  The unstructured free text is the prime source of healthcare information documenting providers’ interpretations of health conditions, diagnoses, medical interventions, impressions, etc.  In order to uncover unknown information and search for patterns in health data with computational methods, we need to structure the unstructured free text data.  For that, we use information extraction, a computational technique for analyzing free text and deriving structured information.  Extracted information from free text can be represented…