references.bib

@article{wu_clusterprofiler_2021,
	title = {{clusterProfiler} 4.0: A universal enrichment tool for interpreting omics data},
	volume = {2},
	issn = {2666-6758},
	url = {https://www.sciencedirect.com/science/article/pii/S2666675821000667},
	doi = {10.1016/j.xinn.2021.100141},
	shorttitle = {{clusterProfiler} 4.0},
	abstract = {Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, {clusterProfiler} 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that {clusterProfiler} 4.0 will be applied to a wide range of scenarios across diverse organisms.},
	pages = {100141},
	number = {3},
	journaltitle = {The Innovation},
	shortjournal = {The Innovation},
	author = {Wu, Tianzhi and Hu, Erqiang and Xu, Shuangbin and Chen, Meijun and Guo, Pingfan and Dai, Zehan and Feng, Tingze and Zhou, Lang and Tang, Wenli and Zhan, Li and Fu, Xiaocong and Liu, Shanshan and Bo, Xiaochen and Yu, Guangchuang},
	urldate = {2021-08-15},
	date = {2021-08-28},
	langid = {english},
	keywords = {biological knowledge mining, {clusterProfiler}, enrichment analysis, functional analysis, visualization},
	file = {ScienceDirect Full Text PDF:/data/Zotero/storage/QSZ7ZHE4/Wu et al. - 2021 - clusterProfiler 4.0 A universal enrichment tool f.pdf:application/pdf;ScienceDirect Snapshot:/data/Zotero/storage/Y44NPVQP/S2666675821000667.html:text/html}
}


@article{yu_gosemsim_2020,
	title = {Gene Ontology Semantic Similarity Analysis Using {GOSemSim}},
	volume = {2117},
	issn = {1940-6029},
	doi = {10.1007/978-1-0716-0301-7_11},
	abstract = {The {GOSemSim} package, an R-based tool within the Bioconductor project, offers several methods based on information content and graph structure for measuring semantic similarity among {GO} terms, gene products and gene clusters. In this chapter, I illustrate the use of {GOSemSim} on a list of regulators in preimplantation embryos. A step-by-step analysis was provided as well as instructions on interpretation and visualization of the results. {GOSemSim} is open-source and is available from https://www.bioconductor.org/packages/{GOSemSim} .},
	pages = {207--215},
	journaltitle = {Methods in Molecular Biology (Clifton, N.J.)},
	shortjournal = {Methods Mol. Biol.},
	author = {Yu, Guangchuang},
	date = {2020},
	pmid = {31960380},
	keywords = {Functional prediction, Gene ontology, {GOSemSim}, Reproducible research, Semantic similarity}
}

@article{yu_meshes_2018,
	title = {Using meshes for {MeSH} term enrichment and semantic analyses},
	volume = {34},
	issn = {1367-4803},
	url = {https://academic.oup.com/bioinformatics/article/34/21/3766/5001391},
	doi = {10.1093/bioinformatics/bty410},
	abstract = {{AbstractSummary}.  Medical Subject Headings ({MeSH}) is the {NLM} controlled vocabulary used to manually index articles for {MEDLINE}/{PubMed}. {MeSH} provides unique and},
	pages = {3766--3767},
	number = {21},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Yu, Guangchuang},
	urldate = {2019-01-03},
	date = {2018-11-01},
	langid = {english},
	file = {Yu - 2018 - Using meshes for MeSH term enrichment and semantic.pdf:/data/Zotero/storage/PQEK5SWT/Yu - 2018 - Using meshes for MeSH term enrichment and semantic.pdf:application/pdf}
}

@article{yu_dose_2015,
	title = {{DOSE}: an R/Bioconductor package for disease ontology semantic and enrichment analysis},
	volume = {31},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org.eproxy2.lib.hku.hk/content/31/4/608},
	doi = {10.1093/bioinformatics/btu684},
	shorttitle = {{DOSE}},
	abstract = {Summary: Disease ontology ({DO}) annotates human genes in the context of disease. {DO} is important annotation in translating molecular findings from high-throughput data to clinical relevance. {DOSE} is an R package providing semantic similarity computations among {DO} terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported.
Availability and implementation: {DOSE} is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/{DOSE}.html).
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: gcyu@connect.hku.hk or tqyhe@jnu.edu.cn},
	pages = {608--609},
	number = {4},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Yu, Guangchuang and Wang, Li-Gen and Yan, Guang-Rong and He, Qing-Yu},
	urldate = {2015-02-13},
	date = {2015-02-15},
	langid = {english}
}

@article{yu2012,
	title = {{clusterProfiler:} an R Package for Comparing Biological Themes Among Gene Clusters},
	volume = {16},
	issn = {1536-2310, 1557-8100},
	shorttitle = {{clusterProfiler}},
	url = {http://online.liebertpub.com/doi/abs/10.1089/omi.2011.0118},
	doi = {10.1089/omi.2011.0118},
	number = {5},
	urldate = {2012-05-05},
	journal = {{OMICS:} A Journal of Integrative Biology},
	author = {Yu, Guangchuang and Wang, Li-Gen and Han, Yanyan and He, Qing-Yu},
	month = may,
	year = {2012},
	pages = {284--287},
	file = {2012-OMICS-clusterProfiler.pdf:/Volumes/YGC/MyZotero/storage/3ZEKB39T/2012-OMICS-clusterProfiler.pdf:application/pdf;clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters | Abstract:/Volumes/YGC/MyZotero/storage/WPTJGAT7/omi.2011.html:text/html}
}


@Article{yu2011,
	title = {Functional similarity analysis of human virus-encoded {miRNAs}},
	volume = {1},
	issn = {2043-9113},
	url = {http://www.jclinbioinformatics.com/content/1/1/15},
	doi = {10.1186/2043-9113-1-15},
	number = {1},
	journal = {Journal of Clinical Bioinformatics},
	author = {Yu, Guangchuang and He, {Qing-Yu}},
	month = may,
	year = {2011},
	pages = {15}
}

@Article{yu_new_2011,
	title = {A new method for measuring functional similarity of microRNAs},
	volume = {1},
	issn = {2182-0287},
	url = {http://www.jiomics.com/index.php/jio/article/view/21},
	doi = {10.5584/jiomics.v1i1.21},
	number = {1},
	journal = {Journal of Integrated {OMICS}},
	author = {Yu, Guangchuang and Xiao, {Chuan-Le} and Bo, Xiaochen and Lu, {Chun-Hua} and Qin, Yide and Zhan, Sheng and He, {Qing-Yu}},
	month = feb,
	year = {2011},
	keywords = {Clustering, Gene, {microRNA;}, Ontology;, Semantic, Similarity;},
	pages = {49--54}
}

@Article{yu2010,
	title = {GOSemSim: an R package for measuring semantic similarity among GO terms and gene products},
	volume = {26},
	issn = {1367-4803},
	url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/7/976},
	doi = {10.1093/bioinformatics/btq064},
	journal = {Bioinformatics},
	author = {Guangchuang Yu and Fei Li and Yide Qin and Xiaochen Bo and Yibo Wu and Shengqi Wang},
	month = April,
	year = {2010},
	note = {PMID: 20179076},
	keywords = {Gene Ontology, Semantic Similarity, Bioconductor},
	pages = {976-978}
}

@Article{wang_new_2007,
	title = {A new method to measure the semantic similarity of GO terms},
	volume = {23},
	issn = {1460-2059},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17344234},
	doi = {btm087},
	journal = {Bioinformatics (Oxford, England)},
	author = {James Z Wang and Zhidian Du and Rapeeporn Payattakool and Philip S Yu and Chin-Fu Chen},
	month = may,
	year = {2007},
	note = {PMID: 17344234},
	keywords = {Algorithms,Computational Biology,Databases, Genetic,Humans,Semantics,Terminology as Topic,Vocabulary, Controlled},
	pages = {1274-81}
}

@Article{lord_semantic_2003,
	title = {Semantic similarity measures as tools for exploring the gene ontology},
	issn = {1793-5091},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12603061},
	doi = {12603061},
	journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
	author = {P W Lord and R D Stevens and A Brass and C A Goble},
	year = {2003},
	note = {PMID: 12603061},
	keywords = {Classification,Computational Biology,Databases, Protein,Genomics,Humans,Proteomics,Sequence Alignment},
	pages = {601-12}
}

@Article{jiang_semantic_1997,
	title = {Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy},
	url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008},
	journal={Proceedings of 10th International Conference on Research In Computational Linguistics},
	author = {Jay J. Jiang and David W. Conrath},
	year = {1997}
}

@Article{philip_semantic_1999,
	title = {Semantic Similarity in a Taxonomy: An {Information-Based} Measure and its Application to Problems of Ambiguity in Natural Language},
	volume = {11},
	url = {},
	journal = {Journal of Artificial Intelligence Research},
	author = {Resnik Philip},
	year = {1999},
	pages = {95--130}
}

@Article{lin_information-theoretic_1998,
	title = {An {Information-Theoretic} Definition of Similarity},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.1832},
	doi = {10.1.1.55.1832},
	journal = {In Proceedings of the 15th International Conference on Machine Learning},
	author = {Dekang Lin},
	year = {1998},
	pages = {296---304}
}

@Article{schlicker_new_2006,
	title = {A new measure for functional similarity of gene products based on Gene Ontology},
	volume = {7},
	issn = {1471-2105},
	doi = {1471-2105-7-302},
	journal = {BMC Bioinformatics},
	author = {Andreas Schlicker and Francisco S Domingues and Jörg Rahnenführer and Thomas Lengauer},
	year = {2006},
	note = {PMID: 16776819},
	keywords = {{Algorithms,Artificial} {Intelligence,Databases,} {Protein,Genes,Information} Storage and {Retrieval,Multigene} {Family,Natural} Language {Processing,Proteins,Structure-Activity} {Relationship,User-Computer} {Interface,Vocabulary,} Controlled},
	pages = {302}
}



@article{yu_reactomepa_2016,
	title = {{ReactomePA}: an R/Bioconductor package for reactome pathway analysis and visualization},
	volume = {12},
	issn = {1742-2051},
	url = {http://pubs.rsc.org.eproxy2.lib.hku.hk/en/content/articlelanding/2016/mb/c5mb00663e},
	doi = {10.1039/C5MB00663E},
	shorttitle = {{ReactomePA}},
	abstract = {Reactome is a manually curated pathway annotation database for unveiling high-order biological pathways from high-throughput data. {ReactomePA} is an R/Bioconductor package providing enrichment analyses, including hypergeometric test and gene set enrichment analyses. A functional analysis can be applied to the genomic coordination obtained from a sequencing experiment to analyze the functional significance of genomic loci including cis-regulatory elements and non-coding regions. Comparison among different experiments is also supported. Moreover, {ReactomePA} provides several visualization functions to produce highly customizable, publication-quality figures. The source code and documents of {ReactomePA} are freely available through Bioconductor (http://www.bioconductor.org/packages/{ReactomePA}).},
	pages = {477--479},
	number = {2},
	journaltitle = {Molecular {BioSystems}},
	shortjournal = {Mol. {BioSyst}.},
	author = {Yu, Guangchuang and He, Qing-Yu},
	urldate = {2016-02-17},
	date = {2016-01-26},
	langid = {english}
}

@article{yu_chipseeker_2015,
         title  = "ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization",
         author = {Yu, Guangchuang and Wang, Li-Gen and He, Qing-Yu},
         journal = "Bioinformatics",
         year    = "2015",
         volume  = "31",
         number  = "14",
         pages   = "2382-2383",
         PMID    = "25765347",
	 url     = {http://bioinformatics.oxfordjournals.org/content/31/14/2382.abstract},
         doi     = "10.1093/bioinformatics/btv145",
}

@article{yu_dose_2015,
	title = {{DOSE}: an R/Bioconductor package for disease ontology semantic and enrichment analysis},
	volume = {31},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/31/4/608},
	doi = {10.1093/bioinformatics/btu684},
	shorttitle = {{DOSE}},
	abstract = {Summary: Disease ontology ({DO}) annotates human genes in the context of disease. {DO} is important annotation in translating molecular findings from high-throughput data to clinical relevance. {DOSE} is an R package providing semantic similarity computations among {DO} terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported.
Availability and implementation: {DOSE} is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/{DOSE}.html).
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: gcyu@connect.hku.hk or tqyhe@jnu.edu.cn},
	pages = {608--609},
	number = {4},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Yu, Guangchuang and Wang, Li-Gen and Yan, Guang-Rong and He, Qing-Yu},
	urldate = {2015-02-13},
	date = {2015-02-15},
	langid = {english}
}

@Article{yu2012,
	title = {clusterProfiler: an R package for comparing biological themes among gene clusters},
	volume = {16},
	issn = {1536-2310},
	number ={5},
	url = {http://online.liebertpub.com/doi/abs/10.1089/omi.2011.0118},
	doi = {10.1089/omi.2011.0118},
	journal = {OMICS: A Journal of Integrative Biology},
	author = {Guangchuang Yu and Le-Gen Wang and Yanyan Han and Qing-Yu He},
	year = {2012},
	month = may,
	note = {},
	keywords = {clusterProfiler, Gene Ontology, KEGG, Enrichment analysis, R package, Cluster comparison},
	pages = {284-287}
}

@article{yu2011,
	title = {Functional similarity analysis of human virus-encoded {miRNAs}},
	volume = {1},
	issn = {2043-9113},
	url = {http://www.jclinbioinformatics.com/content/1/1/15},
	doi = {10.1186/2043-9113-1-15},
	number = {1},
	journal = {Journal of Clinical Bioinformatics},
	author = {Yu, Guangchuang and He, {Qing-Yu}},
	month = may,
	year = {2011},
	pages = {15}
}

@article{yu_new_2011,
	title = {A new method for measuring functional similarity of {microRNAs}},
	volume = {1},
	issn = {2182-0287},
	url = {http://www.jiomics.com/index.php/jio/article/view/21},
	doi = {10.5584/jiomics.v1i1.21},
	number = {1},
	journal = {Journal of Integrated {OMICS}},
	author = {Yu, Guangchuang and Xiao, {Chuan-Le} and Bo, Xiaochen and Lu, {Chun-Hua} and Qin, Yide and Zhan, Sheng and He, {Qing-Yu}},
	month = feb,
	year = {2011},
	keywords = {Clustering, Gene, {microRNA;}, Ontology;, Semantic, Similarity;},
	pages = {49--54}
}

@Article{yu2010,
	title = {GOSemSim: an R package for measuring semantic similarity among GO terms and gene products},
	volume = {26},
	issn = {1367-4803},
	number= {7},
	url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/7/976},
	doi = {10.1093/bioinformatics/btq064},
	journal = {Bioinformatics},
	author = {Guangchuang Yu and Fei Li and Yide Qin and Xiaochen Bo and Yibo Wu and Shengqi Wang},
	month = April,
	year = {2010},
	note = {PMID: 20179076},
	keywords = {Gene Ontology, Semantic Similarity, Bioconductor},
	pages = {976-978}
}

@Article{schmidt2008,
	title = {The Humoral Immune System Has a Key Prognostic Impact in Node-Negative Breast Cancer},
	volume = {68},
	url = {http://cancerres.aacrjournals.org/content/68/13/5405.abstract},
	doi = {10.1158/0008-5472.CAN-07-5206},
	number = {13},
	journal = {Cancer Research},
	author = {Schmidt, Marcus and B?hm, Daniel and von T?rne, Christian and Steiner, Eric and Puhl, Alexander and Pilch, Henryk and Lehr, {Hans-Anton} and Hengstler, Jan G. and K?lbl, Heinz and Gehrmann, Mathias},
	month = jul,
	year = {2008},
	pages = {5405 --5413}
}


@article{schriml_disease_2011,
	title = {Disease Ontology: a backbone for disease semantic integration},
	volume = {40},
	issn = {0305-1048, 1362-4962},
	shorttitle = {Disease Ontology},
	url = {http://nar.oxfordjournals.org/content/40/D1/D940.long},
	doi = {10.1093/nar/gkr972},
	number = {D1},
	urldate = {2012-03-01},
	journal = {Nucleic Acids Research},
	author = {Schriml, L. M. and Arze, C. and Nadendla, S. and Chang, Y.-W. W. and Mazaitis, M. and Felix, V. and Feng, G. and Kibbe, W. A.},
	month = nov,
	year = {2011},
	pages = {D940--D946},
	file = {Disease Ontology: a backbone for disease semantic integration:/Volumes/YGC/MyZotero/storage/RR2FF6MW/D940.html:text/html;Nucl. Acids Res.-2012-Schriml-D940-6.pdf:/Volumes/YGC/MyZotero/storage/TE2GCF7E/Nucl. Acids Res.-2012-Schriml-D940-6.pdf:application/pdf}
}


@article{boyle2004,
	title = {{GO::TermFinder--open} source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes},
	volume = {20},
	issn = {1367-4803},
	shorttitle = {{GO}},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15297299},
	doi = {10.1093/bioinformatics/bth456},
	number = {18},
	journal = {Bioinformatics (Oxford, England)},
	author = {Boyle, Elizabeth I and Weng, Shuai and Gollub, Jeremy and Jin, Heng and Botstein, David and Cherry, J Michael and Sherlock, Gavin},
	month = dec,
	year = {2004},
	note = {PMID: 15297299},
	keywords = {Abstracting and Indexing as Topic, Database Management Systems, Databases, Protein, Gene Expression Profiling, Information Storage and Retrieval, Natural Language Processing, Oligonucleotide Array Sequence Analysis, Programming Languages, Software, {User-Computer} Interface, Vocabulary, Controlled},
	pages = {3710--3715}
}

@article{luo_pathview,
	title = {Pathview: an {R/Bioconductor} package for pathway-based data integration and visualization},
	volume = {29},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/29/14/1830},
	doi = {10.1093/bioinformatics/btt285},
	shorttitle = {Pathview},
	language = {en},
	issue = {14},
	pages = {1830-1831},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Luo, Weijun and Brouwer, Cory},
	urldate = {2013-10-21},
	date = {2013-07-15},
	note = {{PMID:} 23740750},
}

@article{subramanian_gene_2005,
	title = {Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles},
	volume = {102},
	shorttitle = {Gene set enrichment analysis},
	url = {http://www.pnas.org/content/102/43/15545.abstract},
	doi = {10.1073/pnas.0506580102},
	abstract = {Although genomewide {RNA} expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis ({GSEA)} for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how {GSEA} yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, {GSEA} reveals many biological pathways in common. The {GSEA} method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.},
	number = {43},
	urldate = {2010-03-12},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K. and Mukherjee, Sayan and Ebert, Benjamin L. and Gillette, Michael A. and Paulovich, Amanda and Pomeroy, Scott L. and Golub, Todd R. and Lander, Eric S. and Mesirov, Jill P.},
	month = oct,
	year = {2005},
	pages = {15545--15550}
}

@article{huang_david_2007,
	title = {The {DAVID} Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists},
	volume = {8},
	issn = {14656906},
	url = {http://genomebiology.com/2007/8/9/R183},
	doi = {10.1186/gb-2007-8-9-r183},
	shorttitle = {The {DAVID} Gene Functional Classification Tool},
	pages = {R183},
	number = {9},
	journaltitle = {Genome Biology},
	author = {Huang, Da and Sherman, Brad T and Tan, Qina and Collins, Jack R and Alvord, W Gregory and Roayaei, Jean and Stephens, Robert and Baseler, Michael W and Lane, H Clifford and Lempicki, Richard A},
	urldate = {2015-03-06},
	date = {2007}
}

@article{fresno_rdavidwebservice_2013,
	title = {{RDAVIDWebService}: a versatile R interface to {DAVID}},
	volume = {29},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org.eproxy1.lib.hku.hk/content/29/21/2810},
	doi = {10.1093/bioinformatics/btt487},
	shorttitle = {{RDAVIDWebService}},
	abstract = {Summary: The {RDAVIDWebService} package provides a class-based interface from R programs/scripts to fully access/control the database for annotation, visualization and integrated discovery, without the need for human interaction on its Web site (http://david.abcc.ncifcrf.gov). The library enhances the database for annotation, visualization and integrated discovery capabilities for Gene Ontology analysis by means of {GOstats}-based direct acyclic graph conversion methods, in addition to the usual many-genes-to-many-terms visualization.
Availability and implementation: {RDAVIDWebService} is available as an R package from the Bioconductor project (www.bioconductor.org) and on the authors’ Web site (www.bdmg.com.ar) under {GPL}-2 license, subjected to the terms of use of {DAVID} (http://david.abcc.ncifcrf.gov/content.jsp?file={WS}.html).
Contact: cfresno@bdmg.com.ar or efernandez@bdmg.com.ar},
	pages = {2810--2811},
	number = {21},
	journaltitle = {Bioinformatics},
	shortjournal = {Bioinformatics},
	author = {Fresno, Cristóbal and Fernández, Elmer A.},
	urldate = {2015-03-06},
	date = {2013-11-01},
	langid = {english},
	pmid = {23958726}
}

@article{paranjpe_genome_wide_2013,
	title = {A genome-wide survey of maternal and embryonic transcripts during Xenopus tropicalis development},
	volume = {14},
	issn = {1471-2164},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907017/},
	doi = {10.1186/1471-2164-14-762},
	abstract = {Background
Dynamics of polyadenylation vs. deadenylation determine the fate of several developmentally regulated genes. Decay of a subset of maternal {mRNAs} and new transcription define the maternal-to-zygotic transition, but the full complement of polyadenylated and deadenylated coding and non-coding transcripts has not yet been assessed in Xenopus embryos.

Results
To analyze the dynamics and diversity of coding and non-coding transcripts during development, both polyadenylated {mRNA} and ribosomal {RNA}-depleted total {RNA} were harvested across six developmental stages and subjected to high throughput sequencing. The maternally loaded transcriptome is highly diverse and consists of both polyadenylated and deadenylated transcripts. Many maternal genes show peak expression in the oocyte and include genes which are known to be the key regulators of events like oocyte maturation and fertilization. Of all the transcripts that increase in abundance between early blastula and larval stages, about 30\% of the embryonic genes are induced by fourfold or more by the late blastula stage and another 35\% by late gastrulation. Using a gene model validation and discovery pipeline, we identified novel transcripts and putative long non-coding {RNAs} ({lncRNA}). These {lncRNA} transcripts were stringently selected as spliced transcripts generated from independent promoters, with limited coding potential and a codon bias characteristic of noncoding sequences. Many {lncRNAs} are conserved and expressed in a developmental stage-specific fashion.

Conclusions
These data reveal dynamics of transcriptome polyadenylation and abundance and provides a high-confidence catalogue of novel and long non-coding {RNAs}.},
	pages = {762},
	journaltitle = {{BMC} Genomics},
	shortjournal = {{BMC} Genomics},
	author = {Paranjpe, Sarita S and Jacobi, Ulrike G and van Heeringen, Simon J and C Veenstra, Gert Jan},
	urldate = {2015-03-06},
	date = {2013-11-06},
	pmid = {24195446},
	pmcid = {PMC3907017}
}

@article{omer_ncg,
	title = {NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings},
	volume = {44},
	issn = {0305-1048, 1362-4962},
	shorttitle = {Network of Cancer Gene},
	url = {http://nar.oxfordjournals.org/content/44/D1/D992},
	doi = {10.1093/nar/gkv1123},
	number = {D1},
	urldate = {},
	journal = {Nucleic Acids Research},
	author = {Omer A. and Giovanni M. D. and Thanos P. M. and Francesca D. C.},
	month = jan,
	year = {2016},
	pages = {D992--D999}
}

@article{janet_disgenet,
	title = {DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes},
	volume = {2015},
	issn = {1758-0463},
	shorttitle = {DisGeNET},
	url = {http://database.oxfordjournals.org/content/2015/bav028.long},
	doi = {10.1093/database/bav028},
	number = {},
	urldate = {},
	journal = {Database},
	author = {Janet, P. and Núria Q.R. and Àlex B. and Jordi D.P. and Anna B.M. and Martin B. and Ferran S. and Laura I. F.},
	month = mar,
	year = {2015},
	pages = {bav028}
}



@Article{Du15062009,
        author = {Du, Pan and Feng, Gang and Flatow, Jared and Song, Jie and Holko, Michelle and Kibbe, Warren A. and Lin, Simon M.}, 
title = {From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations},
volume = {25}, 
number = {12}, 
pages = {i63-i68}, 
year = {2009}, 
doi = {10.1093/bioinformatics/btp193}, 
abstract ={Subjective methods have been reported to adapt a general-purpose ontology for a specific application. For example, Gene Ontology (GO) Slim was created from GO to generate a highly aggregated report of the human-genome annotation. We propose statistical methods to adapt the general purpose, OBO Foundry Disease Ontology (DO) for the identification of gene-disease associations. Thus, we need a simplified definition of disease categories derived from implicated genes. On the basis of the assumption that the DO terms having similar associated genes are closely related, we group the DO terms based on the similarity of gene-to-DO mapping profiles. Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms. A compactness-scalable fuzzy clustering method is then applied to group similar DO terms. To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results. As such, the DO terms are aggregated and the redundant DO terms are largely removed. Using these methods, we constructed a simplified vocabulary list from the DO called Disease Ontology Lite (DOLite). We demonstrated that DOLite results in more interpretable results than DO for gene-disease association tests. The resultant DOLite has been used in the Functional Disease Ontology (FunDO) Web application at http://www.projects.bioinformatics.northwestern.edu/fundo.Contact: s-lin2@northwestern.edu}, 
URL = {http://bioinformatics.oxfordjournals.org/content/25/12/i63.abstract}, 
eprint = {http://bioinformatics.oxfordjournals.org/content/25/12/i63.full.pdf+html}, 
journal = {Bioinformatics} 
}

@article{korotkevich_fast_2019,
	title = {Fast gene set enrichment analysis},
	rights = {© 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), {CC} {BY} 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/060012v2},
	doi = {10.1101/060012},
	abstract = {Preranked gene set enrichment analysis ({GSEA}) is a widely used method for interpretation of gene expression data in terms of biological processes. Here we present {FGSEA} method that is able to estimate arbitrarily low {GSEA} P-values with a higher accuracy and much faster compared to other implementations. We also present a polynomial algorithm to calculate {GSEA} P-values exactly, which we use to practically confirm the accuracy of the method.},
	pages = {060012},
	journaltitle = {{bioRxiv}},
	author = {Korotkevich, Gennady and Sukhov, Vladimir and Sergushichev, Alexey},
	urldate = {2020-09-08},
	date = {2019-10-22},
	langid = {english},
	note = {Publisher: Cold Spring Harbor Laboratory
Section: New Results},
	file = {Full Text PDF:/data/Zotero/storage/5IQPK3KB/Korotkevich et al. - 2019 - Fast gene set enrichment analysis.pdf:application/pdf}
}