Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks
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{"title"=>"Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks", "type"=>"journal", "authors"=>[{"first_name"=>"Takeshi", "last_name"=>"Hase", "scopus_author_id"=>"24398902200"}, {"first_name"=>"Samik", "last_name"=>"Ghosh", "scopus_author_id"=>"9334966200"}, {"first_name"=>"Ryota", "last_name"=>"Yamanaka", "scopus_author_id"=>"55758495400"}, {"first_name"=>"Hiroaki", "last_name"=>"Kitano", "scopus_author_id"=>"7201618005"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"24278007", "doi"=>"10.1371/journal.pcbi.1003361", "sgr"=>"84888228308", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "scopus"=>"2-s2.0-84888228308", "issn"=>"1553734X", "pui"=>"370341747"}, "id"=>"66bcf79e-8de1-334e-bf83-5460e712b012", "abstract"=>"Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.", "link"=>"http://www.mendeley.com/research/harnessing-diversity-towards-reconstructing-large-scale-gene-regulatory-networks", "reader_count"=>68, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>7, "Researcher"=>17, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>4, "Professor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>7, "Researcher"=>17, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>4, "Professor"=>5}, "reader_count_by_subject_area"=>{"Engineering"=>9, "Unspecified"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>9, "Agricultural and Biological Sciences"=>32, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>2, "Computer Science"=>12, "Energy"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>9}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Energy"=>{"Energy"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>12}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>5, "Norway"=>1, "United Kingdom"=>1, "Slovenia"=>1, "India"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1292866"], "description"=>"1<p>In silico Dream 5 dataset.</p>2<p>Dream 5 dataset from <i>E.coli</i>.</p>3<p>Dream5 dataset from <i>S.cerevisiae</i>.</p>", "links"=>[], "tags"=>["dream5", "datasets"], "article_id"=>860254, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.t001", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_DREAM5_datasets_used_in_this_study_/860254", "title"=>"The DREAM5 datasets used in this study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292863"], "description"=>"<p>(<b>A</b>) Datasets. Expression datasets were split into a dataset for which optimal algorithms are unknown (e.g., Data1) and datasets for which optimal algorithms are known (e.g., Data2 and Data3). (<b>B</b>) Confidence scores of links between two genes. For each of datasets, confidence scores from each of algorithms (e.g., algorithms, A1, A2, A3, A4, and A5) were calculated. (<b>C</b>) Diversity among algorithms. By using confidence scores calculated in (B), diversity among algorithms were calculated for each of three datasets. In this example, we examined five algorithms and thus, for each of the datasets, we have a vector of 10 distances between two algorithms. (<b>D</b>) Similarity between two expression datasets. Correlation coefficient between the vector of algorithm distances from Data1 and that from Data2 was calculated. The calculated correlation coefficient is defined as similarity between Data1 and Data2. In the example in this figure, Data1 is more similar to Data2 than Data3. Thus, optimal algorithms for Data2 could perform better than those for Data3 to infer GRN from Data1.</p>", "links"=>[], "tags"=>[], "article_id"=>860251, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g006", "stats"=>{"downloads"=>3, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_a_method_to_calculate_similarity_between_two_expression_datasets_/860251", "title"=>"Overview of a method to calculate similarity between two expression datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292885", "https://ndownloader.figshare.com/files/1292886", "https://ndownloader.figshare.com/files/1292887", "https://ndownloader.figshare.com/files/1292888", "https://ndownloader.figshare.com/files/1292890", "https://ndownloader.figshare.com/files/1292891", "https://ndownloader.figshare.com/files/1292892", "https://ndownloader.figshare.com/files/1292893", "https://ndownloader.figshare.com/files/1292894", "https://ndownloader.figshare.com/files/1292895", "https://ndownloader.figshare.com/files/1292896"], "description"=>"<div><p>Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, Top<i>k</i>Net that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) Top<i>k</i>Net integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, Top<i>k</i>Net integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, Top<i>k</i>Net, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.</p></div>", "links"=>[], "tags"=>["reconstructing"], "article_id"=>860263, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003361.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003361.s011"], "stats"=>{"downloads"=>12, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Harnessing_Diversity_towards_the_Reconstructing_of_Large_Scale_Gene_Regulatory_Networks_/860263", "title"=>"Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292857"], "description"=>"<p>(<b>A</b>) Confidence scores from algorithms. Confidence score of a link between two genes were generated by each of three algorithms. In this case, each algorithm has 6 confidence scores for 6 links. Note that the three algorithms in this example are algorithms to infer non-directional algorithms and make symmetrical matrices of confidence scores, i.e., confidence score of link from gene1 to gene 2 is same as that from gene2 to gene1. Thus, for simplicity, upper triangles of confidence score matrices are not shown in the figure. (<b>B</b>) Diversity among algorithms based on Euclidean distances. In this example, each of three algorithms has a vector of 6 confidence scores for 6 links between two genes. Euclidean distance between two vectors of confidence scores from two algorithms is calculated and is defined as diversity between the two algorithms. (<b>C</b>) Diversity among algorithms based on 2nd and 3rd components of PCA analysis. In this example, PCA analysis is conducted on three vectors of 6 confidence scores from three network-inference algorithms and the three algorithms are mapped on to 2nd and 3rd principal components (see left panel of C). Euclidean distance between two algorithms is calculated by using the 2nd and 3rd principal components and is defined as diversity between the two algorithms.</p>", "links"=>[], "tags"=>[], "article_id"=>860245, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_toy_example_to_calculate_diversity_among_algorithms_/860245", "title"=>"A toy example to calculate diversity among algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292852"], "description"=>"<p>Black squares and lines show performances of Top<i>k</i>Net algorithm. For example, values at <i>k</i> = 1 represent performances of Top1Net algorithm. Red and green lines represent performances of community prediction and those of the best algorithm, respectively. (<b>A</b>) Overall score. (<b>B</b>) AUC-PR for in silico dataset. (<b>C</b>) AUC-ROC for in silico dataset. (<b>D</b>) Max f-score for in silico dataset. (<b>E</b>) AUC-PR for <i>E. coli</i> dataset. (<b>F</b>) AUC-ROC for <i>E. coli</i> dataset. (<b>G</b>) Max f-score for <i>E. coli</i> dataset. (<b>H</b>) AUC-PR for <i>S. cerevisiae</i> dataset. (<b>I</b>) AUC-ROC for <i>S. cerevisiae</i> dataset. (<b>J</b>) Max f-score for <i>S. cerevisiae</i> dataset.</p>", "links"=>[], "tags"=>["38", "network-inference"], "article_id"=>860240, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g002", "stats"=>{"downloads"=>1, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_Top_k_Net_and_community_prediction_based_on_integration_of_the_38_network_inference_algorithms_/860240", "title"=>"Performances of Top<i>k</i>Net and community prediction based on integration of the 38 network-inference algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292865"], "description"=>"<p>Red lines show performance of Top<i>k</i>Net integrating algorithms that are optimal for a dataset with high-similarity, while green lines show that with low-similarity. Blue lines show performance of Top<i>k</i>Net integrating top 10 highest-performance algorithms. Dashed lines in red, green, and blue represent performance of community prediction integrating algorithms that are optimal for a dataset with high-similarity, that with low-similarity, and top 10 highest-performance algorithms, respectively. (<b>A</b>) AUC-PR for <i>E. coli</i> dataset. (<b>B</b>) AUC-ROC for <i>E. coli</i> dataset. (<b>C</b>) Max f-score for <i>E. coli</i> dataset. (<b>D</b>) AUC-PR for <i>S. cerevisiae</i> dataset. (<b>E</b>) AUC-ROC for <i>S. cerevisiae</i> dataset. (<b>F</b>) Max f-score for <i>S. cerevisiae</i> dataset.</p>", "links"=>[], "tags"=>["algorithm", "datasets", "network-inference"], "article_id"=>860253, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g008", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_algorithm_selection_based_on_similarity_among_expression_datasets_and_its_potential_to_improve_network_inference_accuracy_/860253", "title"=>"Optimal algorithm selection based on similarity among expression datasets and its potential to improve network-inference accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292850"], "description"=>"<p>(<b>a</b>) Target Network. Circles and links are genes and regulatory links among genes, respectively. (<b>b</b>) The five lists are ranked according to the confidence levels of links, the most reliable prediction is at the top of the list and has the highest rank, <i>i.e.</i>, Algorithm1 assigns the highest confidence level and the rank value of 1 to a link between nodes 1 and 2. The true link of the target network is highlighted in yellow. We regard links with rank of 1–7 as regulatory links inferred by the algorithms because the target network composed of 7 links. Red lines and blue dashed lines represent true positive and false negative links, respectively. (<b>c</b>) Five rank values for each link and the mean value among the five values. Green, red, orange, blue, and purple represent rank values from Algorithm1, Algorithm2, Algorithm3, Algorithm4, and Algorithm5, respectively. (<b>d</b>) Rank value of a link by Top<i>k</i>Net and that by Community Prediction. Top1Net and Top2Net regards 1st and 2nd highest value among five rank values for a link as the rank value of the link, respectively. Community Prediction calculates the mean value among five rank values for a link and regards the mean as the rank of the link. For example, rank of the links between genes 1 and 2 for Community Prediction is 7.4. This example illustrates how Top1Net can be more accurate than the other algorithms.</p>", "links"=>[], "tags"=>["formed"], "article_id"=>860238, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g001", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_a_prediction_by_Top_k_Net_formed_from_five_individual_network_predictions_/860238", "title"=>"Example of a prediction by Top<i>k</i>Net formed from five individual network predictions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292864"], "description"=>"<p>The scatter plots show correlation of algorithm distance between two gene-expression datasets. Each of points in scatter plots represents each of algorithm pairs. Because we have 703 algorithm pairs among 38 algorithms, 703 points are in each of the figures. Vertical axis represents (EUC or PCA) distance between two algorithms for one gene-expression dataset, while horizontal axis represents that for the other gene-expression dataset. (<b>A</b>) Scatter plots of EUC distance for in silico and <i>E. coli</i> datasets. (<b>B</b>) Scatter plots of EUC distance for in silico and <i>S. cerevisiae</i> dataset. (<b>C</b>) Scatter plots of EUC distance for <i>E. coli</i> and <i>S. cerevisiae</i> datasets (<b>D</b>) Scatter plots of PCA distance for in silico and <i>E. coli</i> datasets. (<b>E</b>) Scatter plots of PCA distance for in silico and <i>S. cerevisiae</i> datasets. (<b>F</b>) Scatter plots of PCA distance for <i>E. coli</i> and <i>S. cerevisiae</i> datasets.</p>", "links"=>[], "tags"=>["gene-expression", "datasets", "algorithm"], "article_id"=>860252, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g007", "stats"=>{"downloads"=>0, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Similarity_among_gene_expression_datasets_based_on_algorithm_diversity_/860252", "title"=>"Similarity among gene-expression datasets based on algorithm diversity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292859"], "description"=>"<p>H and L represent high-diversity and low-diversity algorithm pairs, respectively. (<b>A</b>) Box-plots of overall score. (<b>B</b>) Box-plots of AUC-PR for in silico dataset. (<b>C</b>) Box-plots of AUC-ROC for in silico dataset. (<b>D</b>) Box-plots of Max f-score for in silico dataset. (<b>E</b>) Box-plots of AUC-PR for <i>E. coli</i> dataset. (<b>F</b>) Box-plots of AUC-ROC for <i>E. coli</i> dataset. (<b>G</b>) Box-plots of Max f-score for <i>E. coli</i> dataset. (<b>H</b>) Box-plots of AUC-PR for <i>S. cerevisiae</i> dataset. (<b>I</b>) Box-plots of AUC-ROC for <i>S. cerevisiae</i> dataset. (<b>J</b>) Box-plots of max f-score for <i>S. cerevisiae</i> dataset. * and ** represent P<0.05 and P<0.01, by the Wilcoxon rank sum test.</p>", "links"=>[], "tags"=>["top1net", "high-", "low-diversity", "algorithm", "pairs", "euc"], "article_id"=>860247, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g005", "stats"=>{"downloads"=>1, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_Top1Net_based_on_integration_of_high_or_low_diversity_algorithm_pairs_by_EUC_distance_/860247", "title"=>"Performances of Top1Net based on integration of high- or low-diversity algorithm pairs by EUC distance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292854"], "description"=>"<p>Black squares and lines show performances of Top<i>k</i>Net algorithm. For example, values at <i>k</i> = 1 represent performances of Top1Net algorithm. Red and green lines represent performances of community prediction and those of the best algorithm, respectively. (<b>A</b>) Overall score. (<b>B</b>) AUC-PR for in silico dataset. (<b>C</b>) AUC-ROC for in silico dataset. (<b>D</b>) Max f-score for in silico dataset. (<b>E</b>) AUC-PR for <i>E. coli</i> dataset. (<b>F</b>) AUC-ROC for <i>E. coli</i> dataset. (<b>G</b>) Max f-score for <i>E. coli</i> dataset. (<b>H</b>) AUC-PR for <i>S. cerevisiae</i> dataset. (<b>I</b>) AUC-ROC for <i>S. cerevisiae</i> dataset. (<b>J</b>) Max f-score for <i>S. cerevisiae</i> dataset.</p>", "links"=>[], "tags"=>["10", "highest-performance"], "article_id"=>860242, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.g003", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_Top_k_Net_and_Community_prediction_based_on_integration_of_top_10_highest_performance_algorithms_/860242", "title"=>"Performances of Top<i>k</i>Net and Community prediction based on integration of top 10 highest-performance algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-11-21 05:08:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1292867"], "description"=>"1<p>Spearman's correlation coefficient of algorithm distance (EUC distance) between Dataset 1 and Dataset 2.</p>2<p>Spearman's correlation coefficient of algorithm distance (PCA distance) between Dataset 1 and Dataset 2.</p>3<p>In silico Dream 5 dataset.</p>4<p>Dream 5 dataset from <i>E.coli</i>.</p>5<p>Dream5 dataset from <i>S.cerevisiae</i>.</p>", "links"=>[], "tags"=>["coefficient", "algorithm", "distances", "metrics", "dream5", "gene-expression"], "article_id"=>860255, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Takeshi Hase", "Samik Ghosh", "Ryota Yamanaka", "Hiroaki Kitano"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003361.t002", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_coefficient_of_algorithm_distances_and_that_of_performance_metrics_across_the_DREAM5_gene_expression_datasets_/860255", "title"=>"Correlation coefficient of algorithm distances and that of performance metrics across the DREAM5 gene-expression datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-11-21 05:08:14"}

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Relative Metric

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