Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge
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{"title"=>"Gene expression prediction by soft integration and the elastic net - Best performance of the DREAM3 gene expression challenge", "type"=>"journal", "authors"=>[{"first_name"=>"Mika", "last_name"=>"Gustafsson", "scopus_author_id"=>"35614799200"}, {"first_name"=>"Michael", "last_name"=>"Hörnquist", "scopus_author_id"=>"35277296800"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"20169069", "sgr"=>"77949535261", "doi"=>"10.1371/journal.pone.0009134", "scopus"=>"2-s2.0-77949535261", "pui"=>"358466943", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "issn"=>"19326203"}, "id"=>"cf987e17-f850-32bf-a79c-bc024942a284", "abstract"=>"BACKGROUND: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. METHODOLOGY/PRINCIPAL FINDINGS: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the \"elastic net\". Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. CONCLUSIONS/SIGNIFICANCE: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.", "link"=>"http://www.mendeley.com/research/gene-expression-prediction-soft-integration-elastic-net-best-performance-dream3-gene-expression-chal", "reader_count"=>48, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>17, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>4, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>17, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>4, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Mathematics"=>3, "Agricultural and Biological Sciences"=>22, "Medicine and Dentistry"=>4, "Physics and Astronomy"=>2, "Computer Science"=>11}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>11}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "Hong Kong"=>1, "Belgium"=>1, "United States"=>5, "United Kingdom"=>3}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/863921"], "description"=>"<p>The correlations are all with respect to the gold standard. The upper blue curve (stars) is our result; the green curve slightly below (rings) belongs to Ruan <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009134#pone.0009134-Ruan1\" target=\"_blank\">[11]</a>, while the lower red curve (plus-signs) is the mean of all other participants. The connecting lines are only guides for the eye. Most of the times, these results co-vary, but occasionally they have even different signs. The two breaks of the mean curve (gene numbers 13 and 38) come from some non-valid numbers in the presented results.</p>", "links"=>[], "tags"=>["computational biology/systems biology", "computer science/systems and control theory", "mathematics/statistics"], "article_id"=>534376, "categories"=>["Mathematics", "Medicine", "Information And Computing Sciences"], "users"=>["Mika Gustafsson", "Michael Hörnquist"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009134.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_rank_correlation_for_each_gene_/534376", "title"=>"Spearman rank correlation for each gene.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:44:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/864091"], "description"=>"<p>The correlations are based on cross-validations, where the last column stands for an overall calculation based on 24 ranking lists. A minimization of least squares, combined with a penalty term of the form of the elastic net, gives the best performance.</p>", "links"=>[], "tags"=>["correlations", "inference"], "article_id"=>534549, "categories"=>["Mathematics", "Medicine", "Information And Computing Sciences"], "users"=>["Mika Gustafsson", "Michael Hörnquist"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009134.t002", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_rank_correlations_based_on_two_different_inference_schemes_/534549", "title"=>"Spearman rank correlations based on two different inference schemes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:45:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/864042"], "description"=>"<p>The correlations are based on cross-validations, where the last column stands for an overall calculation based on 24 ranking lists. The minimization with respect to an L2-norm has the best performance, both for including only expression values and for including both expression values and rates. Following the principle of including as little as possible, we discard the rates.</p>", "links"=>[], "tags"=>["correlations", "predictions", "fits", "minimization", "l1-"], "article_id"=>534502, "categories"=>["Mathematics", "Medicine", "Information And Computing Sciences"], "users"=>["Mika Gustafsson", "Michael Hörnquist"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009134.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_rank_correlations_for_predictions_obtained_by_perfect_fits_and_minimization_of_L1_and_L2_norms_/534502", "title"=>"Spearman rank correlations for predictions obtained by perfect fits and minimization of L1- and L2-norms.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:45:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/863858"], "description"=>"<p>The correlations are all with respect to the gold standard. The upper blue curve (stars) is our result; the green curve slightly below (rings) belongs to Ruan <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009134#pone.0009134-Ruan1\" target=\"_blank\">[11]</a>, while the lower red curve (plus-signs) is the mean of all other participants. The connecting lines are only guides for the eye. Note how the rankings for some time points obviously are harder to predict than others, and that the results are clearly co-varying.</p>", "links"=>[], "tags"=>["computational biology/systems biology", "computer science/systems and control theory", "mathematics/statistics"], "article_id"=>534319, "categories"=>["Mathematics", "Medicine", "Information And Computing Sciences"], "users"=>["Mika Gustafsson", "Michael Hörnquist"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009134.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_rank_correlation_for_each_time_point_/534319", "title"=>"Spearman rank correlation for each time point.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:44:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/864068"], "description"=>"<p>The expression data are obtained from the Rosetta Inpharmatics and ncbi omnibus, and integrated into the inference process by more terms in the objective function. The TF-binding data come from Yeastract and form priors for the penalty term, making it more probable that genes which are co-regulated should act as predictors for each other. Both data sets are only included to the extent the cross-validation procedure allows.</p>", "links"=>[], "tags"=>["correlations"], "article_id"=>534536, "categories"=>["Mathematics", "Medicine", "Information And Computing Sciences"], "users"=>["Mika Gustafsson", "Michael Hörnquist"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009134.t003", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spearman_rank_correlations_after_soft_integration_of_other_data_sets_/534536", "title"=>"Spearman rank correlations after soft integration of other data sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:45:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/863995"], "description"=>"<p>We utilize as prior information that genes which are co-regulated are likely to be effective predictors of each other; the more TFs in common, the more likely to be co-regulated.</p>", "links"=>[], "tags"=>["weighed"], "article_id"=>534456, "categories"=>["Mathematics", "Medicine", "Information And Computing Sciences"], "users"=>["Mika Gustafsson", "Michael Hörnquist"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009134.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_view_of_how_to_obtain_the_weighed_shared_fraction_of_TFs_/534456", "title"=>"Schematic view of how to obtain the weighed shared fraction of TFs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:45:07"}

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Relative Metric

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