Oct. 23, 2020
FatPlants: A Comprehensive Website Platform of Plant Fat Related Genes, Proteins and Metabolism
Increasing seed oil content by plant breeding has resulted in trade-offs or penalties with respect to protein content, seed size, or seed set. The molecular basis for this impasse is mostly speculative. Use of current global profiling approaches to better understand both the metabolic consequences of higher oil and the basis for reduced yield must also deal with off-target genetic mutations (even in near-isogenic lines), ultimately confounding cause-effect interpretations. We propose a diverse, integrated strategy to study the consequences of higher lipid production by studying transgenic plants specifically engineered to produce higher seed oil. As a part of this collaborative project,…
Oct. 20, 2020
Cancer Surveillance Informatics – Missouri Cancer Registry and Research Center
Cancer surveillance has to be able to monitor trends of cancer cases, subtypes of cancer, and cancer clusters to inform cancer control and policies related to cancer and cancer care. The increasing complexity and depth of cancer care, treatment, and other elements to describe a cancer case require cancer registries to capture data from a wide variety of sources by utilizing many informatics methods to integrate these data. Current and future developments include automated processing of electronic pathology reports, artificial intelligence, natural language processing NLP, and linkage of data sources to expand surveillance goals. Recent developments include a national virtual…
Oct. 14, 2020
Development of A Blockchain Framework for Virtual Clinical Trials
Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. “Blockchain,” a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable “Smart Contract” feature makes blockchain more suitable and feasible for VCT by solving computational…
Oct. 14, 2020
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…
Oct. 2, 2020
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…
Oct. 2, 2020
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…
Sep. 29, 2020
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.
Sep. 28, 2020
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…
Sep. 21, 2020
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,…