scholarly journals CREATE: A New Data Resource to Support Cardiac Precision Health

CJC Open ◽  
2020 ◽  
Author(s):  
Seungwon Lee ◽  
Bing Li ◽  
Elliot A. Martin ◽  
Adam G. D'Souza ◽  
Jason Jiang ◽  
...  
Keyword(s):  
Data Resource ◽  
Precision Health

D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Léa El Khoury ◽  
David Mobley
Keyword(s):  
Binding Mode ◽  
Energy Estimates ◽  
Poor Correlation ◽  
Grand Challenge ◽  
Pose Prediction ◽  
Design Data ◽  
Data Resource ◽  
Affinity Ranking ◽  
Ligand Similarity ◽  
Bace 1

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

Biologicals in allergic diseases and asthma: Toward personalized medicine and precision health: Highlights of the 3rd EAACI Master Class on Biologicals, San Lorenzo de El Escorial, Madrid, 2019

Allergy ◽  
2019 ◽  
Vol 75 (4) ◽  
pp. 936-940 ◽  
Author(s):  
Oscar Palomares ◽  
Eva Untersmayr ◽  
Jan Gutermuth ◽  
Ioana Agache ◽  
Sofia Ajeganova ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Allergic Diseases ◽  
San Lorenzo ◽  
Precision Health

scholarly journals The open targets post-GWAS analysis pipeline

2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  
Keyword(s):  
Drug Targets ◽  
Gene Expression Regulation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Data Resource ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Genes ◽  
Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

Technology assessment framework for precision health applications

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
M. Sazzad Hussain ◽  
David Silvera-Tawil ◽  
Geremy Farr-Wharton
Keyword(s):  
Emerging Technologies ◽  
Mobile Apps ◽  
Scientific Evidence ◽  
Wearable Sensors ◽  
Assessment Process ◽  
Assessment Framework ◽  
Response Options ◽  
Regulatory Standards ◽  
Precision Health ◽  
Health Applications

Abstract Objective Established and emerging technologies—such as wearable sensors, smartphones, mobile apps, and artificial intelligence—are shaping positive healthcare models and patient outcomes. These technologies have the potential to become precision health (PH) innovations. However, not all innovations meet regulatory standards or have the required scientific evidence to be used for health applications. In response, an assessment framework was developed to facilitate and standardize the assessment of innovations deemed suitable for PH. Methods A scoping literature review undertaken through PubMed and Google Scholar identified approximately 100 relevant articles. These were then shortlisted (n = 12) to those that included specific metrics, criteria, or frameworks for assessing technologies that could be applied to the PH context. Results The proposed framework identified nine core criteria with subcriteria and grouped them into four categories for assessment: technical, clinical, human factors, and implementation. Guiding statements with response options and recommendations were used as metrics against each criterion. Conclusion The proposed framework supports health services, health technology innovators, and researchers in leveraging current and emerging technologies for PH innovations. It covers a comprehensive set of criteria as part of the assessment process of these technologies.

scholarly journals Science for the Next Century: Deep Phenotyping

2021 ◽  
pp. 002203452110018
Author(s):  
J.T. Wright ◽  
M.C. Herzberg
Keyword(s):  
Health Care ◽  
Human Health ◽  
Care Delivery ◽  
The Past ◽  
Large Populations ◽  
Health And Disease ◽  
Methodological Approaches ◽  
Omic Data ◽  
Precision Health ◽  
Deep Phenotyping

Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by the recent availability of high-throughput genomics and multi-omics analyses and the companion tools of advanced informatics and computational science. Understanding of the human genome and its influence on phenotype continues to advance through genotyping large populations and using “light phenotyping” approaches in combination with smaller subsets of the population being evaluated using “deep phenotyping” approaches. Using our capability to integrate and jointly analyze genomic data with other multi-omic data, the knowledge of genotype-phenotype relationships and associated genetic pathways and functions is being advanced. Understanding genotype-phenotype relationships that discriminate human health from disease is speculated to facilitate predictive, precision health care and change modes of health care delivery. The American Association for Dental Research Fall Focused Symposium assembled experts to discuss how studies of genotype-phenotype relationships are illuminating the pathophysiology of craniofacial diseases and developmental biology. Although the breadth of the topic did not allow all areas of dental, oral, and craniofacial research to be addressed (e.g., cancer), the importance and power of integrating genomic, phenomic, and other -omic data are illustrated using a variety of examples. The 8 Fall Focused talks presented different methodological approaches for ascertaining study populations and evaluating population variance and phenotyping approaches. These advances are reviewed in this summary.

scholarly journals BRENDA, the ELIXIR core data resource in 2021: new developments and updates

2020 ◽  
Vol 49 (D1) ◽  
pp. D498-D508
Author(s):  
Antje Chang ◽  
Lisa Jeske ◽  
Sandra Ulbrich ◽  
Julia Hofmann ◽  
Julia Koblitz ◽  
...  
Keyword(s):  
Intracellular Localization ◽  
Easy Access ◽  
New Developments ◽  
Data Resource ◽  
Core Data ◽  
Related Data ◽  
Primary Literature ◽  
Ligand Interactions ◽  
Genome Annotations ◽  
Individual Enzyme

Abstract The BRENDA enzyme database (https://www.brenda-enzymes.org), established in 1987, has evolved into the main collection of functional enzyme and metabolism data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted from ∼157 000 primary literature references, combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, ligand information, taxonomic, bibliographic, and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.

scholarly journals Data resource profile: JMDC claims database sourced from health insurance societies

2021 ◽  
Author(s):  
Katsuhiko Nagai ◽  
Takashi Tanaka ◽  
Norihisa Kodaira ◽  
Shinya Kimura ◽  
Yoshimitsu Takahashi ◽  
...  
Keyword(s):  
Health Insurance ◽  
Data Resource ◽  
Claims Database
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