CiteULike | Further Information

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Mendeley | Further Information

{"title"=>"Novel approach to meta-analysis of microarray datasets reveals muscle remodeling-related drug targets and biomarkers in Duchenne muscular dystrophy", "type"=>"journal", "authors"=>[{"first_name"=>"Ekaterina", "last_name"=>"Kotelnikova", "scopus_author_id"=>"55947048800"}, {"first_name"=>"Maria A.", "last_name"=>"Shkrob", "scopus_author_id"=>"55220566400"}, {"first_name"=>"Mikhail A.", "last_name"=>"Pyatnitskiy", "scopus_author_id"=>"24480283200"}, {"first_name"=>"Alessandra", "last_name"=>"Ferlini", "scopus_author_id"=>"56942300000"}, {"first_name"=>"Nikolai", "last_name"=>"Daraselia", "scopus_author_id"=>"6603466966"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "scopus"=>"2-s2.0-84861129090", "sgr"=>"84861129090", "pui"=>"364830960", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"22319435", "doi"=>"10.1371/journal.pcbi.1002365"}, "id"=>"1d20a201-f137-315c-bf00-e145831d24d8", "abstract"=>"Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets.The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA) to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes.We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD). In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1), regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A) correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers.", "link"=>"http://www.mendeley.com/research/novel-approach-metaanalysis-microarray-datasets-reveals-muscle-remodelingrelated-drug-targets-biomar", "reader_count"=>87, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>33, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>4, "Student > Master"=>6, "Other"=>7, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>33, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>4, "Student > Master"=>6, "Other"=>7, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>9, "Mathematics"=>1, "Medicine and Dentistry"=>17, "Agricultural and Biological Sciences"=>44, "Neuroscience"=>1, "Arts and Humanities"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>3, "Chemistry"=>1, "Social Sciences"=>2, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>17}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>44}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Mathematics"=>{"Mathematics"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Korea (South)"=>1, "United States"=>3, "United Kingdom"=>1, "Moldova"=>1, "Slovenia"=>1, "France"=>1}, "group_count"=>4}

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• http://doi.org/10.1016/j.yexcr.2013.12.020
• http://doi.org/10.1016/j.ygcen.2014.01.018
• http://doi.org/10.1093/hmg/dds219
• http://doi.org/10.1093/bib/bbv092
• http://doi.org/10.5504/BBEQ.2013.0083
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• http://doi.org/10.3233/JND-150102
• http://doi.org/10.1016/j.nmd.2012.09.002
• http://doi.org/10.1371/journal.pone.0059093
• http://doi.org/10.1080/00913847.2019.1684810
• http://doi.org/10.1186/1752-0509-7-139
• http://doi.org/10.1096/fj.13-238071
• http://doi.org/10.1016/j.aquatox.2014.11.022
• http://doi.org/10.1016/j.mehy.2015.08.015
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• http://doi.org/10.1016/j.toxrep.2016.03.009
• http://doi.org/10.1016/j.aquatox.2018.06.005
• http://doi.org/10.1186/1471-2164-15-1028
• http://doi.org/10.1016/j.cbd.2016.02.001
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• http://doi.org/10.1371/journal.pone.0091272
• http://doi.org/10.1016/j.mod.2012.12.002
• http://doi.org/10.1016/j.nmd.2013.07.009
• http://doi.org/10.1016/j.neuro.2012.09.012
• http://doi.org/10.1016/j.toxlet.2013.03.031
• http://doi.org/10.1016/j.aquatox.2014.08.008
• http://doi.org/10.1109/JPROC.2015.2507119
• http://doi.org/10.1016/j.molmed.2013.07.002

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• {"id"=>"240794521561079809", "text"=>"#PLOS: Novel Approach to Meta-Analysis of Microarray Datasets Reveals Muscle Remodeling-r... http://t.co/8zjkUaiQ", "created_at"=>"2012-08-29T12:54:22Z", "user"=>"yannabraham", "user_name"=>"Yann Abraham", "user_profile_image"=>"http://a0.twimg.com/profile_images/54359750/spaceman_spiff_2_normal.jpg"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/686048"], "description"=>"<p>GEO datasets used for the meta-analysis.</p>", "links"=>[], "tags"=>["datasets"], "article_id"=>356539, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002365.t003", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_GEO_datasets_used_for_the_meta_analysis_/356539", "title"=>"GEO datasets used for the meta-analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-02-02 01:48:59"}
• {"files"=>["https://ndownloader.figshare.com/files/685745"], "description"=>"<p>See corresponding section for detailed description.</p>", "links"=>[], "tags"=>["workflow"], "article_id"=>356236, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002365.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overall_workflow_of_the_analysis_/356236", "title"=>"Overall workflow of the analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-02 01:43:56"}
• {"files"=>["https://ndownloader.figshare.com/files/349743", "https://ndownloader.figshare.com/files/349765"], "description"=>"<div><p>Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets.</p> <p>The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA) to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes.</p> <p>We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD). In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1), regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A) correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers.</p> </div>", "links"=>[], "tags"=>["meta-analysis", "microarray", "datasets", "reveals", "remodeling-related", "targets", "biomarkers", "duchenne", "muscular", "dystrophy"], "article_id"=>129137, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002365.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002365.s002"], "stats"=>{"downloads"=>8, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Novel_Approach_to_Meta_Analysis_of_Microarray_Datasets_Reveals_Muscle_Remodeling_related_Drug_Targets_and_Biomarkers_in_Duchenne_Muscular_Dystrophy/129137", "title"=>"Novel Approach to Meta-Analysis of Microarray Datasets Reveals Muscle Remodeling-related Drug Targets and Biomarkers in Duchenne Muscular Dystrophy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-02-02 02:32:17"}
• {"files"=>["https://ndownloader.figshare.com/files/685933"], "description"=>"<p>For each of six datasets and for each type of regulation gene ranking procedure was performed and overlap between six top-k lists was calculated. Fraction of common genes in top-k reaches saturation for k roughly equal to 500, hence adding more genes will not increase overlap between six rankings.</p>", "links"=>[], "tags"=>["genes", "top-k", "rankings", "types"], "article_id"=>356411, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002365.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fraction_of_common_genes_in_top_k_rankings_for_different_types_of_gene_expression_/356411", "title"=>"Fraction of common genes in top-k rankings for different types of gene expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-02 01:46:51"}
• {"files"=>["https://ndownloader.figshare.com/files/686026"], "description"=>"<p>Consistent regulators of differentially expressed genes plus regulators from SNEA of reference dataset.</p>", "links"=>[], "tags"=>["regulators", "differentially", "genes", "snea"], "article_id"=>356508, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002365.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Consistent_regulators_of_differentially_expressed_genes_plus_regulators_from_SNEA_of_reference_dataset_/356508", "title"=>"Consistent regulators of differentially expressed genes plus regulators from SNEA of reference dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-02-02 01:48:28"}
• {"files"=>["https://ndownloader.figshare.com/files/685824"], "description"=>"<p>Most of SNEA-derived regulators of down-regulated genes regulate the processes related to myotube formation, fast-to-slow fiber type switch (including changes in myofiber composition, mitochondria content and insulin sensitivity) and metabolic changes in DMD affected muscles. Relations are described in text. Catalytic subunit of AMPK, PRKAA2, is shown next to AMPK. Functional class - class of proteins, such as enzyme families. Complex - a group of two or more proteins linked by non-covalent protein-protein interactions. Expression - protein members of one class regulate expression of proteins in another class. DirectRegulation - protein members of one class bind and regulate proteins in another class. Regulation - protein members of one class indirectly regulate proteins in another class. ProteinModification - protein members of the regulator class phosphorylate or otherwise modify proteins in the target class. PromoterBinding - protein members of one class bind promoters of genes encoding proteins in another class.</p>", "links"=>[], "tags"=>["down-regulated"], "article_id"=>356313, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002365.g002", "stats"=>{"downloads"=>3, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regulators_of_down_regulated_genes_/356313", "title"=>"Regulators of down-regulated genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-02 01:45:13"}
• {"files"=>["https://ndownloader.figshare.com/files/686088"], "description"=>"<p>Biological processes from Gene Ontology associated with consistently differentially expressed genes were found by applying “Find groups enriched with selected entities” tool embedded in Ariadne Pathway Studio to the list of 431 genes. Resulting significant (p-value<0.05) biological processes were sorted by number of genes involved in a process. Top 10 processes are shown.</p>", "links"=>[], "tags"=>["ontology", "groups", "enriched", "differentially"], "article_id"=>356572, "categories"=>["Cell Biology", "Neuroscience", "Medicine", "Pharmacology", "Biological Sciences", "Genetics"], "users"=>["Ekaterina Kotelnikova", "Maria A. Shkrob", "Mikhail A. Pyatnitskiy", "Alessandra Ferlini", "Nikolai Daraselia"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002365.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_Ontology_groups_enriched_by_consistent_differentially_expressed_genes_/356572", "title"=>"Gene Ontology groups enriched by consistent differentially expressed genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-02-02 01:49:32"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime