scholarly journals Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice Uncovers Signs and Symptoms Associated with Terrestrial Diseases

Life ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 42
Author(s):  
Charlotte A. Nelson ◽  
Ana Uriarte Acuna ◽  
Amber M. Paul ◽  
Ryan T. Scott ◽  
Atul J. Butte ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Signs And Symptoms ◽  
Knowledge Network ◽  
Knowledge Graph ◽  
Space Travel ◽  
Omics Data ◽  
Physiological Changes ◽  
Network Embedding ◽  
Transcriptomic Data ◽  
Heterogeneous Knowledge

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.

MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine

2018 ◽  
Vol 169 (3) ◽  
pp. 625-632 ◽  
Author(s):  
Bingbing Xie ◽  
Zifeng Yuan ◽  
Yadong Yang ◽  
Zhidan Sun ◽  
Shuigeng Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Precision Medicine ◽  
Omics Data ◽  
Comprehensive Database

General Management Principles of the Pregnant Woman

2017 ◽  
Vol 38 (02) ◽  
pp. 123-134
Author(s):  
Margaret Miller ◽  
Amanpreet Kaur
Keyword(s):  
Cardiopulmonary Arrest ◽  
Patient Positioning ◽  
Signs And Symptoms ◽  
Diagnostic Procedures ◽  
Pregnant Patient ◽  
Physiological Changes ◽  
Limited Data ◽  
Chest Compressions ◽  
General Management ◽  
In Pregnancy

AbstractPregnancy is a dynamic process that consists of profound physiological changes mediated by hormonal, mechanical, and circulatory pathways. Understanding of changes in physiology is essential for distinguishing abnormal and normal signs and symptoms in a pregnant patient. These physiological changes also have important pharmacotherapeutic considerations for a pregnant patient. Although there are limited data to guide decisions regarding medications and diagnostic procedures in pregnancy, a careful review of risks should be balanced with review of risk of withholding a medication or procedure. Interventional pulmonary procedures can be safely performed in pregnant women while keeping in mind the maternal anatomic and physiologic changes. Furthermore, management of a maternal cardiopulmonary arrest requires important modifications in patient positioning and intravenous access to ensure adequate efficacy of chest compressions, circulation, and airway management. This review will provide an overview of maternal physiologic changes with a focus on cardiopulmonary physiology, pharmacotherapeutic considerations, diagnostic and interventional pulmonary procedures during pregnancy, and cardiopulmonary resuscitation in pregnancy.

scholarly journals The knowledge graph as the default data model for learning on heterogeneous knowledge

Data Science ◽  
2017 ◽  
Vol 1 (1-2) ◽  
pp. 39-57 ◽  
Author(s):  
Xander Wilcke ◽  
Peter Bloem ◽  
Victor de Boer
Keyword(s):  
Data Model ◽  
Knowledge Graph ◽  
Default Data ◽  
Heterogeneous Knowledge

scholarly journals Metastatic Site Prediction in Breast Cancer using Omics Knowledge Graph and Pattern Mining with Kirchhoff’s Law Traversal

2020 ◽  
Author(s):  
Alokkumar Jha ◽  
Yasar Khan ◽  
Ratnesh Sahay ◽  
Mathieu d’Aquin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Pattern Mining ◽  
Knowledge Graph ◽  
Omics Data ◽  
Metastatic Site ◽  
Decision Network ◽  
Knowledge Graphs ◽  
Metastatic Sites ◽  
Kirchhoff's Law

AbstractPrediction of metastatic sites from the primary site of origin is a impugn task in breast cancer (BRCA). Multi-dimensionality of such metastatic sites - bone, lung, kidney, and brain, using large-scale multi-dimensional Poly-Omics (Transcriptomics, Proteomics and Metabolomics) data of various type, for example, CNV (Copy number variation), GE (Gene expression), DNA methylation, path-ways, and drugs with clinical associations makes classification of metastasis a multi-faceted challenge. In this paper, we have approached the above problem in three steps; 1) Applied Linked data and semantic web to build Poly-Omics data as knowledge graphs and termed them as cancer decision network; 2) Reduced the dimensionality of data using Graph Pattern Mining and explained gene rewiring in cancer decision network by first time using Kirchhoff’s law for knowledge or any graph traversal; 3) Established ruled based modeling to understand the essential -Omics data from poly-Omics for breast cancer progression 4) Predicted the disease’s metastatic site using Kirchhoff’s knowledge graphs as a hidden layer in the graph convolution neural network(GCNN). The features (genes) extracted by applying Kirchhoff’s law on knowledge graphs are used to predict disease relapse site with 91.9% AUC (Area Under Curve) and performed detailed evaluation against the state-of-the-art approaches. The novelty of our approach is in the creation of RDF knowledge graphs from the poly-omics, such as the drug, disease, target(gene/protein), pathways and application of Kirchhoff’s law on knowledge graph to and the first approach to predict metastatic site from the primary tumor. Further, we have applied the rule-based knowledge graph using graph convolution neural network for metastasis site prediction makes the even classification novel.

scholarly journals Omics-based Hybrid Prediction in Maize

2017 ◽  
Author(s):  
Matthias Westhues ◽  
Tobias A. Schrag ◽  
Claas Heuer ◽  
Georg Thaller ◽  
H. Friedrich Utz ◽  
...  
Keyword(s):  
Conflict Of Interest ◽  
Developmental Stages ◽  
Agronomic Traits ◽  
Genomic Data ◽  
Hybrid Breeding ◽  
Correct Prediction ◽  
Eqtl Analysis ◽  
Omics Data ◽  
Success Rates ◽  
Transcriptomic Data

AbstractAccurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction (WGP) has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream “omics” data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of “omics” data. Here, we evaluate several “omics” predictors — genomic, transcriptomic and metabolic data — measured on parent lines at early developmental stages, and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream “omics” data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.Key messageComplementing genomic data with other “omics” predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.Conflict of InterestThe authors declare that they have no conflict of interest.

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