Identifying Patient-specific Flow of Signal Transduction Perturbed by Multiple Single-nucleotide Alterations

Background: Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations is critical for improving patient outcomes in cancer cases. However, accurate estimation of mutational effects at the pathway level for such patients remains an open problem. While probabilistic pathway topology methods are gaining interest among the scientific community, the overwhelming majority do not account for network perturbation effects from multiple single-nucleotide alterations.   Methods: Here we present an improvement of the Mutational Forks formalism to infer the patient-specific flow of signal transduction based on multiple single-nucleotide alterations, including non-synonymous and synonymous mutations. The lung adenocarcinoma and skin cutaneous melanoma datasets from…

Analysis of Healthy Coping Feedback Messages from Diabetes Mobile Apps

To analyze Healthy Coping-related feedback messages from diabetes mobile apps against the theoretical framework based on behavioral change theories. We searched apps using the search terms: “diabetes,” “blood sugar,” “glucose,” and “mood” from iTunes and Google Play stores. We entered a range of values on three Healthy Coping domains: 1) diabetes-related measures, 2) physical exercise/activity, and 3) mood to generate feedback messages. We developed behavioral change theory-based framework and analyzed the feedback messages against three dimensions of timing, intention, and content (feedback purpose and feedback response). The feedback purposes were categorized into seven purposes; warning, suggestion, self-monitoring, acknowledging, reinforcement, goal…

Data-Driven Patients Stratification and Drug Repositioning

Cancer is a heterogeneous disease and represents a great example of the need for selecting patient-centric rather than disease-centric treatment. De novo drug discovery is a time-consuming and high-cost process with a low success rate. Drug repositioning (DR) reduces the time, cost, and risk of developing new drugs because it recommends new uses for drugs already declared safe for human use. This study uses a novel data mining approach, exploratory data mining (EDM), and network analysis for patient cohort stratification and DR. This approach enables identifying homogeneous subgroups within a heterogeneous disease population and recommends drugs based on subgroup-specific molecular…

Dr. DeSouza Appointed to Associate Editor Position for IEEE Transactions on Artificial Intelligence

Dr. Gui DeSouza, MUIDSI core faculty member and co-leader of the Data-Driven Ag Initiative, has been appointed as an associate editor to the new journal IEEE Transactions on Artificial Intelligence. The Institute of Electrical and Electronics Engineers plans to launch the journal next month. It’s a joint publication among several IEEE societies, with IEEE’s Computational Intelligence Society (CIS) taking the lead. To read more about the journal and Dr. DeSouza’s appointment, click here.

Data Analytics Using the Cerner Real-world COVID-19 Data to Answer Clinical Questions

Cerner Real World data allows researchers and data scientists to explore clinical data via HealtheDataLab. Recently, Cerner granted many of their client institutions access to de-identified patient’s data for COVID-19 research to help fight the pandemic.  The de-identified patient data was dataset contains COVID-19 related encounters, demographics, chronic conditions, medication, and lab results. This large-scale cohort allows for the investigation of the relationships between risk factors, comorbidities, complication and specific outcomes. However, due to variable accuracy and completeness of data, usage of EHR data for research purposes requires an in-depth understanding of potential pitfalls which could lead to inaccurate or unrepresentative…

Improving interpretation of Genome-Wide Association Studies (GWAS) by quantifying Marker Recurrence

To understand biological differences within groups of people or animals, we often turn to DNA. A genome-wide association study (GWAS) can assess the genetic contribution of biological differences between individuals. However, the scale of input data continues to expand in three ways: the sequence coverage of genomes, the number of individuals sequenced, and the number of phenotype records per individual. High-throughput workflows are computationally intensive and require a laborious interpretation of results. These barriers inhibit systematically investigating hypotheses and limit the effective translation of genetics into biomedical and agricultural solutions. The expansion of data analyzed, compounded by numerous analysis approaches,…

Implementing GeoARK: The Geospatial Analytical Research Knowledgebase

This research focuses on the development and implementation of an interface to the Geospatial Analytical Research Knowledgebase (GeoARK), a spatially enabled big data informatics approach assembled around applications in health research and analytics. Example applications in telehealth reach, COVID-19 risk in rural situations, pathways for zoonotic disease spread, and contextual leukemia research will be provided. The creation and design of GeoARK occurred within the University of Missouri’s Institute for Data Science and Informatics. Being spatially engendered, its core is data that is pre-processed, cleaned, integrated and represented in its spatial context as millions of point locations. To this core, additional…

An R-based platform for the visualization and analysis of single molecule tracking experiments

Single molecule tracking (SMT) is a technique of single-molecule fluorescence imaging that allows for the exploration of molecular motion at a high spatiotemporal resolution on living cells. This is widely used to define dynamics of individual tumor cell-surface receptors. Spatiotemporal regulation of many of these receptors varies across cancer types, playing a key role in tumor progression and drug resistance. Many tools can be used to identify trajectories and calculate their features from these experiments. However, there are relatively few tools to analyze this data. Thus, the present study uses a set of live-cell single-molecule imaging experiments with a model…

MUIDSI and MUIDSI Graduate Student Organization to Host Virtual Symposium August 20th-21st

Join us on August 20th-21st as we host our annual Data Science and Informatics Symposium online via Zoom. This year we welcome four keynote speakers, a keynote panel, six faculty research talks, four student presentations, a start-up panel, and a COVID-19 panel featuring the latest contributions from MU faculty and students. For a full schedule click here. For free registration, please visit our Eventbrite site.