scholarly journals Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research

F1000Research ◽  
2014 ◽  
Vol 2 ◽  
pp. 30 ◽  
Author(s):  
Sebastian Köhler ◽  
Sandra C Doelken ◽  
Barbara J Ruef ◽  
Sebastian Bauer ◽  
Nicole Washington ◽  
...  
Keyword(s):  
Generation Process ◽  
Tissue Formation ◽  
Phenotype Ontology ◽  
Data Generation ◽  
Gene Set Enrichment ◽  
Human Phenotype ◽  
Research Activities ◽  
Annotation Data ◽  
Human Genes ◽  
Phenotype Data

Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species.We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases.This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.

scholarly journals Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 30 ◽  
Author(s):  
Sebastian Köhler ◽  
Sandra C Doelken ◽  
Barbara J Ruef ◽  
Sebastian Bauer ◽  
Nicole Washington ◽  
...  
Keyword(s):  
Generation Process ◽  
Tissue Formation ◽  
Phenotype Ontology ◽  
Data Generation ◽  
Gene Set Enrichment ◽  
Research Activities ◽  
Annotation Data ◽  
Human Genes ◽  
Phenotype Data ◽  
Phenotype Annotation

Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species.We have generated a cross-species phenotype ontology for human, mouse and zebra fish that contains zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases.This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.

scholarly journals The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

2013 ◽  
Vol 42 (D1) ◽  
pp. D966-D974 ◽  
Author(s):  
Sebastian Köhler ◽  
Sandra C. Doelken ◽  
Christopher J. Mungall ◽  
Sebastian Bauer ◽  
Helen V. Firth ◽  
...  
Keyword(s):  
Molecular Biology ◽  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Phenotype Data

scholarly journals Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Jean-Philippe F Gourdine ◽  
Matthew H Brush ◽  
Nicole A Vasilevsky ◽  
Kent Shefchek ◽  
Sebastian Köhler ◽  
...  
Keyword(s):  
Rare Diseases ◽  
Semantic Analysis ◽  
Model Organism ◽  
Disease Diagnosis ◽  
Knowledge Bases ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Biological Ontologies ◽  
Phenotype Data ◽  
Structuring Knowledge

Abstract While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype–phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about the annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.

scholarly journals Phenopolis: an open platform for harmonization and analysis of sequencing and phenotype data

2016 ◽  
Author(s):  
Nikolas Pontikos ◽  
Jing Yu ◽  
Fiona Blanco-Kelly ◽  
Tom Vulliamy ◽  
Tsz Lun Wong ◽  
...  
Keyword(s):  
Supplementary Information ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Open Platform ◽  
Link Type ◽  
Variant Filtering ◽  
Phenotype Data ◽  
Supplementary Material ◽  
Intuitive Interface ◽  
Gene Prioritisation

AbstractSummaryPhenopolis is an open-source web server which provides an intuitive interface to genetic and phenotypic databases. It integrates analysis tools which include variant filtering and gene prioritisation based on phenotype. The Phenopolis platform will accelerate clinical diagnosis, gene discovery and encourage wider adoption of the Human Phenotype Ontology in the study of rare disease.Availability and ImplementationA demo of the website is available at http://phenopolis.github.io (username: demo, password: demo123). If you wish to install a local copy, souce code and installation instruction are available at https://github.com/pontikos/phenopolis. The software is implemented using Python, MongoDB, HTML/Javascript and various bash shell [email protected] informationhttp://phenopolis.github.io

Hierarchical Concept-Driven Language Model

2021 ◽  
Vol 15 (6) ◽  
pp. 1-22
Author(s):  
Yashen Wang ◽  
Huanhuan Zhang ◽  
Zhirun Liu ◽  
Qiang Zhou
Keyword(s):  
Language Model ◽  
Generation Process ◽  
Data Generation ◽  
Modeling Framework ◽  
Long Distance ◽  
Short Text ◽  
Proposed Model ◽  
Scalable Inference ◽  
End To End ◽  
Hidden Layer

For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

Predicting genes from phenotypes using Human Phenotype Ontology (HPO) terms

2021 ◽  
Vol 132 ◽  
pp. S149
Author(s):  
Anne Slavotinek ◽  
Hannah Prasad ◽  
Hannah Hoban ◽  
Tiffany Yip ◽  
Shannon Rego ◽  
...  
Keyword(s):  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Human Phenotype

scholarly journals Worm Phenotype Ontology: Integrating phenotype data within and beyond the C. elegans community

2011 ◽  
Vol 12 (1) ◽  
pp. 32 ◽  
Author(s):  
Gary Schindelman ◽  
Jolene S Fernandes ◽  
Carol A Bastiani ◽  
Karen Yook ◽  
Paul W Sternberg
Keyword(s):  
Phenotype Ontology ◽  
C Elegans ◽  
Phenotype Data

scholarly journals Low Cost Data Analytics Implementation on Project Management Process Automation

2019 ◽  
Vol 5 (2) ◽  
pp. 76-82
Author(s):  
Cornelius Mellino Sarungu ◽  
Liliana Liliana
Keyword(s):  
Project Management ◽  
Data Analytics ◽  
Management Practice ◽  
Low Cost ◽  
Project Manager ◽  
Management Process ◽  
Generation Process ◽  
Data Generation ◽  
Team Members ◽  
Single Output

Project management practice used many tools to support the process of recording and tracking data generated along the whole project. Project analytics provide deeper insights to be used on decision making. To conduct project analytics, one should explore the tools and techniques required. The mostcommon tool is Microsoft Excel. Its simplicity and flexibility make project manager or project team members can utilize it to do almost any kind of activities. We combine MS Excel with R Studio to brought data analytics into the project management process. While the data input process still using the old way that the project manager already familiar, the analytic engine could extract data from it and create visualization of needed parameters in a single output report file. This kind of approach deliver a low cost solution of project analytics for the organization. We can implement it with relatively low cost technology onone side, some of them are free, while maintaining the simple way of data generation process. This solution can also be proposed to improve project management process maturity level to the next stage, like CMMI level 4 that promote project analytics. Index Terms—project management, project analytics, data analytics.

scholarly journals GenomeDiver: A platform for phenotype-guided medical genomic diagnosis

2020 ◽  
Author(s):  
Nathaniel Pearson ◽  
Christian Stolte ◽  
Kevin Shi ◽  
Faygel Beren ◽  
Noura S. Abul-Husn ◽  
...  
Keyword(s):  
Diagnostic Testing ◽  
Diagnostic Process ◽  
Genomic Sequencing ◽  
Sequencing Data ◽  
Clinical Diagnostic Laboratory ◽  
Diagnostic Laboratory ◽  
Phenotypic Data ◽  
Human Phenotype ◽  
Phenotype Data

ABSTRACTPurposeMaking a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient’s phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient’s disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process.MethodsGenomeDiver is designed to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient’s presentation.ResultsWe show how GenomeDiver communicates the clinician’s informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and an example of the performance of GenomeDiver using simulated genomic testing data.ConclusionsGenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory-clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.

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