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PREDICTING GENE FUNCTION FROM GENE EXPRESSIONS AND ONTOLOGIES

T. R. HVIDSTEN, J. KOMOROWSKI, A. K. SANDVIK, A. LÆGREID
2000 Biocomputing 2001  
doi:10.1142/9789814447362_0030 fatcat:aoiecmem4zdo5f4vvahpcfp5zy

Predicting candidate genes from phenotypes, functions, and anatomical site of expression [article]

Jun Chen, Azza Althagafi, Robert Hoehndorf
2020 biorxiv/medrxiv   pre-print
Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models.  ...  Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression.  ...  of gene products, anatomical site or tissue of gene expression, and phenotypes resulting from a gene's loss of function.  ... 
doi:10.1101/2020.03.30.015594 fatcat:vsr2lpzbmvcz7jesaio7dvrd6i

Predicting gene ontology from a global meta-analysis of 1-color microarray experiments

Mikhail G Dozmorov, Cory B Giles, Jonathan D Wren
2011 BMC Bioinformatics  
Prediction performance was benchmarked by calculating the distance within the Gene Ontology (GO) tree between predicted function and annotated function for sets of 100 randomly selected genes.  ...  Of the 5,720 genes without GO annotation, 4,189 had at least one predicted ontology using top 40 co-expressed genes for prediction analysis.  ...  MGD designed, implemented and tested Gene Ontology concordance and divergence analysis for one-color microarray data. CGB implemented Gene Ontology acyclic graph traversing.  ... 
doi:10.1186/1471-2105-12-s10-s14 pmid:22166114 pmcid:PMC3236836 fatcat:s43xd3plnzftjgwkt5sb627z34

Predicting Candidate Genes From Phenotypes, Functions, And Anatomical Site Of Expression

Jun Chen, Azza Althagafi, Robert Hoehndorf, Peter Robinson
2020 Bioinformatics  
Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models.  ...  Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression.  ...  Funding This work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No.  ... 
doi:10.1093/bioinformatics/btaa879 pmid:33051643 fatcat:3wb4wr6tzvfdxexm7vuva66rmi

The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science

Sebastian Klie, Zoran Nikoloski
2012 Frontiers in Genetics  
In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of co-expression networks and the guilt-by-association  ...  Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes.  ...  on gene function prediction.  ... 
doi:10.3389/fgene.2012.00115 pmid:22754563 pmcid:PMC3384976 fatcat:mn4hgcxjmfa2xcr2wzpqtktzpy

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns

Yongsheng Li, Hong Chen, Tao Pan, Chunjie Jiang, Zheng Zhao, Zishan Wang, Jinwen Zhang, Juan Xu, Xia Li
2015 OncoTarget  
We propose an integrative method for genome-wide functional predictions of lncRNAs by combining chromatin states data with gene expression patterns.  ...  Our approach provides a functional annotation of the lncRNAs, which we developed into a web-based application, LncRNA Ontology, to provide visualization, analysis, and downloading of lncRNA putative functions  ...  features and expression patterns can predict the functions of protein coding genes with high accuracy.  ... 
doi:10.18632/oncotarget.5794 pmid:26485761 pmcid:PMC4741861 fatcat:whc77yt4ubchtoz6gizg3nmh7i

Reinfection of Transplanted Livers in HCV- and HCV/HIV-Infected Patients Is Characterized by a Different MicroRNA Expression Profile

Emiliano Dalla, Michela Bulfoni, Daniela Cesselli, Riccardo Pravisani, Masaaki Hidaka, Susumu Eguchi, Umberto Baccarani
2022 Cells  
The DIANA-miRPath v3.0 webserver and DIANA-microT-CDS algorithm (v5.0) were used to characterize the functions of differentially expressed (DE-) miRNAs, querying the KEGG and Gene Ontology-Biological Process  ...  Instead, HCV-LB and HCV/HIV-LB differed in the expression of miRNAs involved in immunological and apoptotic processes and in extracellular matrix remodeling.  ...  Summary of the "Gene Ontology-Biological Process" enriched functional terms associated with the predicted gene targets of the miRNAs differentially expressed between LB obtained from HCV/HIV co-infected  ... 
doi:10.3390/cells11040690 pmid:35203343 pmcid:PMC8869900 fatcat:7d27wrryw5afbglchfh6cu32ii

A Review of Software for Predicting Gene Function

Swee Kuan Loh, Swee Thing Low, Mohd Saberi Mohamad, Safaai Deris, Shahreen Kasim, Choon Yee Wen, Zuwairie Ibrahim, Bambang Susilo, Yusuf Hendrawan, Agustin Krisna Wardani
2015 International Journal of Bio-Science and Bio-Technology  
High-throughput sequencing can lead to increased understanding of proteins and genes. We can infer networks of functional couplings from direct and indirect interactions.  ...  The development of gene function prediction is one of the major recent advances in the bioinformatics fields.  ...  Acknowledgements We would like to thank the Universiti Teknologi Malaysia for supporting this research through a GUP research grant (Grant number: Q.J130000.2507.05H50) and a Matching grant (Grant number  ... 
doi:10.14257/ijbsbt.2015.7.2.06 fatcat:nedrong7bzhqrgzqhholgfguyq

All systems GO for understanding mouse gene function

Chris Holmes, Steve D M Brown
2004 Journal of Biology  
Now, an extensive analysis of gene expression in the mouse reveals that quantitative measurement of expression levels in different tissues can contribute powerfully to the prediction of gene function.  ...  It is widely supposed that the tissue specificity of gene expression indicates gene function.  ...  [6] provides us with a clear message: a carefully designed study using Gene Ontology and quantitative expression profiles can reveal functional relationships and can be a powerful predictor of gene  ... 
doi:10.1186/jbiol19 pmid:15610553 pmcid:PMC549721 fatcat:b62wwvrmobdchlc363zrlso6vq

Classification of Gene Expression Data in an Ontology [chapter]

Herman Midelfart, Astrid Lægreid, Jan Komorowski
2001 Lecture Notes in Computer Science  
Prediction of gene function from expression profiles is an intriguing problem that has been attempted with both unsupervised clustering and supervised learning methods.  ...  However, even supervised methods ignore the fact that the functional classes associated with genes are typically organized in an ontology.  ...  Conclusions and Future Work The task of predicting gene function from expression profiles creates new challenges for rule learning: The processes are typically organized in an ontology, and hence new learning  ... 
doi:10.1007/3-540-45497-7_28 fatcat:ynufr6spuzboxgh7cjg5qsy2fi

Genes and Gene Ontologies Common to Airflow Obstruction and Emphysema in the Lungs of Patients with COPD

Santiyagu M. Savarimuthu Francis, Jill E. Larsen, Sandra J. Pavey, Edwina E. Duhig, Belinda E. Clarke, Rayleen V. Bowman, Nick K. Hayward, Kwun M. Fong, Ian A. Yang, Neeraj Vij
2011 PLoS ONE  
Class comparison analysis on mild (n = 9, FEV 1 80-110% predicted) and moderate (n = 9, FEV 1 50-60% predicted) COPD patients identified 46 differentially expressed genes (p,0.01), of which 14 genes were  ...  Gene expression profiling was performed on total RNA extracted from lung tissue of 18 former smokers with COPD.  ...  We also appreciate the assistance of the Thoracic Research Laboratory staff, pathology staff and surgeons at The Prince Charles Hospital who were involved in the collection and processing of lung tissue  ... 
doi:10.1371/journal.pone.0017442 pmid:21423603 pmcid:PMC3057973 fatcat:gnzxthsgajbnzgjbayes2uaqwu

Computational methods for direct cell conversion

Uma S. Kamaraj, Julian Gough, Jose M. Polo, Enrico Petretto, Owen J. L. Rackham
2016 Cell Cycle  
We argue that collecting high-quality gene expression data from single-cells or pure cell-populations across a broader set of cell-types would be necessary to improve the quality and consistency of the  ...  Our analysis reveals that the factors predicted by each method tend to be different due to varying source cells used, gene expression quantification and algorithmic steps.  ...  The set of TFs predicted is extended to a network by adding 100 genes based on (1) known gene-gene interactions or co-expression (etc., see above) and (2) to favour a common biological function.  ... 
doi:10.1080/15384101.2016.1238119 pmid:27736295 pmcid:PMC5224461 fatcat:x3vtawa455bfdbzxsqsafe3fbi

Multiple Partial Regularized Nonnegative Matrix Factorization for Predicting Ontological Functions of lncRNAs

Jianbang Zhao, Xiaoke Ma
2019 Frontiers in Genetics  
The current algorithms predict the functions of lncRNA by using the features of protein-coding genes.  ...  The model and algorithm provide an effective way to explore the functions of lncRNAs.  ...  To explore the knowledge from genes, (Liao et al., 2011) combined the expression profiles of lncRNAs and genes to construct a coding and non-coding gene co-expression network according to the expression  ... 
doi:10.3389/fgene.2018.00685 pmid:30728826 pmcid:PMC6351489 fatcat:6gyk4ctcyjgm3lklmbwaxgeyw4

Inferring combinatorial regulation of transcription in silico

N. Bl thgen
2005 Nucleic Acids Research  
Furthermore, starting from the well-characterized promoter of a gene expressed upon lipopolysaccharide stimulation, we predict functional targets of this stimulus.  ...  We demonstrate that for the well-studied set of skeletal muscle-related transcription factors Myf-2, Mef and TEF, the correct functions are predicted.  ...  N.B. acknowledges support from DFG (SFB 618), and Sz.M.K. from BMBF.  ... 
doi:10.1093/nar/gki167 pmid:15647509 pmcid:PMC546154 fatcat:juduu2rm7febnlvpo43ylif6b4

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

Maxat Kulmanov, Robert Hoehndorf, Casey S. Greene
2020 PLoS Computational Biology  
DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions  ...  We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes.  ...  Acknowledgments We acknowledge the use of computational resources from the KAUST Supercomputing Core Laboratory.  ... 
doi:10.1371/journal.pcbi.1008453 pmid:33206638 fatcat:c43pskgozvae5iflwqukglgcoe
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