scholarly journals Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Hu ◽  
Wenyang Zhou ◽  
Jun Ren ◽  
Lixiang Dong ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Human Genome ◽  
Pearson Correlation ◽  
Function Annotation ◽  
Systematic Analysis ◽  
Individual Gene ◽  
Human Genes ◽  
Human Genome Organisation ◽  
Mining Tool ◽  
High Consistency ◽  
Text Mining Tool

Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401,p=2.2e-16) and gene frequency of individual term (Pearson correlation = 0.1298,p=3.686e-14) in GeneRIFs and GOA shows our annotation resource is very reliable.

scholarly journals Arrayed CRISPR reveals genetic regulators of tau aggregation, autophagy and mitochondria in Alzheimer’s disease model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lishu Duan ◽  
Mufeng Hu ◽  
Joseph A. Tamm ◽  
Yelena Y. Grinberg ◽  
Fang Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Discovery ◽  
Disease Model ◽  
Mitochondrial Morphology ◽  
Individual Gene ◽  
Human Genes ◽  
Future Drug ◽  
Tau Aggregation

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease with poor prognosis. New options for drug discovery targets are needed. We developed an imaging based arrayed CRISPR method to interrogate the human genome for modulation of in vitro correlates of AD features, and used this to assess 1525 human genes related to tau aggregation, autophagy and mitochondria. This work revealed (I) a network of tau aggregation modulators including the NF-κB pathway and inflammatory signaling, (II) a correlation between mitochondrial morphology, respiratory function and transcriptomics, (III) machine learning predicted novel roles of genes and pathways in autophagic processes and (IV) individual gene function inferences and interactions among biological processes via multi-feature clustering. These studies provide a platform to interrogate underexplored aspects of AD biology and offer several specific hypotheses for future drug discovery efforts.

scholarly journals The Human Genome Organisation (HUGO) and the 2020 COVID-19 pandemic

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Benjamin Capps ◽  
◽  
Yann Joly ◽  
John Mulvihill ◽  
Won Bok Lee
Keyword(s):  
Human Genome ◽  
Law And Society ◽  
Genome Organisation ◽  
Global Response ◽  
Genomics Research ◽  
Human Genome Organisation ◽  
Ethics Law

AbstractThis letter is the Human Genome Organisation’s summary reaction to the 2020 COVID-19 pandemic. It identifies key areas for genomics research, and areas in which genomic scientists can contribute to a global response to the pandemic. The letter has been reviewed and endorsed by the HUGO Committee on Ethics, Law and Society (CELS) and the HUGO Council.

scholarly journals MedMiner: An Internet Text-Mining Tool for Biomedical Information, with Application to Gene Expression Profiling

BioTechniques ◽  
1999 ◽  
Vol 27 (6) ◽  
pp. 1210-1217 ◽  
Author(s):  
L. Tanabe ◽  
U. Scherf ◽  
L.H. Smith ◽  
J.K. Lee ◽  
L. Hunter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Mining Tool ◽  
Text Mining Tool

scholarly journals SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nícia Rosário-Ferreira ◽  
Victor Guimarães ◽  
Vítor S. Costa ◽  
Irina S. Moreira
Keyword(s):  
Text Mining ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Disease Similarity ◽  
Disease Associations ◽  
Named Entity Normalization ◽  
Mining Tool ◽  
Or Gene ◽  
Text Mining Tool

Abstract Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector

2021 ◽  
Vol 124 ◽  
pp. 103357
Author(s):  
G. Fantoni ◽  
E. Coli ◽  
F. Chiarello ◽  
R. Apreda ◽  
F. Dell’Orletta ◽  
...  
Keyword(s):  
Text Mining ◽  
Technical Specifications ◽  
Mining Tool ◽  
Text Mining Tool

scholarly journals Testing a text mining tool for emerging risk identification

2016 ◽  
Vol 13 (12) ◽  
Author(s):  
Niels B. Lucas Luijckx ◽  
Fred J. van de Brug ◽  
Winfried R. Leeman ◽  
Jos M.B.M. van der Vossen ◽  
Hilde J. Cnossen
Keyword(s):  
Text Mining ◽  
Risk Identification ◽  
Emerging Risk ◽  
Mining Tool ◽  
Text Mining Tool
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