DisPredict: A Predictor of Disordered Protein Using Optimized RBF Kernel
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{"title"=>"DisPredict: A predictor of disordered protein using optimized RBF kernel", "type"=>"journal", "authors"=>[{"first_name"=>"Sumaiya", "last_name"=>"Iqbal", "scopus_author_id"=>"35183110800"}, {"first_name"=>"Md Tamjidul", "last_name"=>"Hoque", "scopus_author_id"=>"57105890400"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"26517719", "doi"=>"10.1371/journal.pone.0141551", "sgr"=>"84950335591", "scopus"=>"2-s2.0-84950335591", "issn"=>"19326203", "pui"=>"607277470"}, "id"=>"b8f16f7d-5319-3389-8a7e-47b965f1b137", "abstract"=>"Intrinsically disordered proteins or, regions perform important biological functions through their dynamic conformations during binding. Thus accurate identification of these disordered regions have significant implications in proper annotation of function, induced fold prediction and drug design to combat critical diseases. We introduce DisPredict, a disorder predictor that employs a single support vector machine with RBF kernel and novel features for reliable characterization of protein structure. DisPredict yields effective performance. In addition to 10-fold cross validation, training and testing of DisPredict was conducted with independent test datasets. The results were consistent with both the training and test error minimal. The use of multiple data sources, makes the predictor generic. The datasets used in developing the model include disordered regions of various length which are categorized as short and long having different compositions, different types of disorder, ranging from fully to partially disordered regions as well as completely ordered regions. Through comparison with other state of the art approaches and case studies, DisPredict is found to be a useful tool with competitive performance. DisPredict is available at https://github.com/tamjidul/DisPredict_v1.0.", "link"=>"http://www.mendeley.com/research/dispredict-predictor-disordered-protein-using-optimized-rbf-kernel", "reader_count"=>5, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Student > Master"=>1, "Lecturer > Senior Lecturer"=>1, "Student > Postgraduate"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Student > Master"=>1, "Lecturer > Senior Lecturer"=>1, "Student > Postgraduate"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Agricultural and Biological Sciences"=>1, "Physics and Astronomy"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>2}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2393065"], "description"=>"<p>The x-axis and y-axis show the Recall(Sensitivity) and Precision (PPV), respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590912, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g006", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Precision_Recall_curves_for_disorder_prediction_on_DD73_dataset_given_by_DisPredict_blue_SPINE_D_green_and_MFDp_red_/1590912", "title"=>"Precision-Recall curves for disorder prediction on DD73 dataset given by DisPredict(<i>blue</i>), SPINE-D(<i>green</i>) and MFDp(<i>red</i>).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393071"], "description"=>"<p>In (P41212, A), the yellow (40 − 124 residues) and pink bar (339 − 420 residues) represent to the PNT domain [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.ref080\" target=\"_blank\">80</a>] and ETS DNA binding region [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.ref081\" target=\"_blank\">81</a>], respectively. In (P01116, B), the orange (10 − 17 residues), cyan (32 − 40 residues) and purple bar (166 − 185 residues) correspond to the GTP binding region [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.ref082\" target=\"_blank\">82</a>], effector region and hypervariable region, respectively. In (P04637, C), the dark green (17 − 25 residues), red (325 − 356 residues) and gray bar (370 − 372 residues) highlight to the TADI motif [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.ref083\" target=\"_blank\">83</a>], oligomer region and [KR]-[STA]-K binding motif, respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590914, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g007", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Disorder_probability_plot_for_A_human_ETV6_P41212_B_human_KRAS_P01116_and_C_human_p53_P04637_proteins_given_by_DisPredict_red_SPINE_D_blue_and_MFDp_green_/1590914", "title"=>"Disorder probability plot for (A) human ETV6 (P41212), (B) human KRAS (P01116) and (C) human p53 (P04637) proteins, given by DisPredict(<i>red</i>), SPINE-D (<i>blue</i>) and MFDp (<i>green</i>).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393074"], "description"=>"<p>Legends are shown for different range of lengths (with interval size 15) and each bar is labeled with total number of occurrence of a disordered region of this specific length.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590916, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g008", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_disordered_regions_of_different_lengths_in_MxD444_left_and_SL477_right_dataset_/1590916", "title"=>"Distribution of disordered regions of different lengths in MxD444 (<i>left</i>) and SL477 (<i>right</i>) dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393076"], "description"=>"<p>The x-axis and y-axis correspond to the probability of having well defined secondary structure (in terms of probability being coil) and fraction of exposed residues of that region, respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590918, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g009", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_plot_between_structural_characterizations_of_ordered_blue_and_disordered_red_regions_within_A_SL477_and_B_MxD444_dataset_/1590918", "title"=>"Correlation plot between structural characterizations of ordered (<i>blue</i>) and disordered (<i>red</i>) regions within (A) SL477 and (B) MxD444 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393078"], "description"=>"<p>The <i>x</i>-axis and <i>y</i>-axis represent the 20 different amino acids and their relative proportions in the composition.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590920, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g010", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percentage_of_amino_acid_type_residues_in_actual_composition_blue_or_left_adjacent_bar_and_predicted_composition_red_or_right_adjacent_bar_of_A_SL171_and_B_MxD134_dataset_/1590920", "title"=>"Percentage of amino acid type residues in actual composition (<i>blue, or left adjacent bar</i>) and predicted composition (<i>red, or right adjacent bar</i>) of (A) SL171 and (B) MxD134 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393079"], "description"=>"<p>List of features used in DisPredict.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590921, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t001", "stats"=>{"downloads"=>1, "page_views"=>31, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_features_used_in_DisPredict_/1590921", "title"=>"List of features used in DisPredict.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393080"], "description"=>"<p>W<sub><i>size</i></sub> indicates the size of sliding window.</p><p>Best values of each metric are marked in bold for each dataset separately.</p><p><sup>1</sup> N<sub><i>correct</i></sub> is reported with total number of residues (Residue<sub><i>total</i></sub>) to be predicted in parentheses. Both of the counts correspond to one subset (fold) of the full dataset which is generated for performing cross validation.</p><p><sup>2</sup> For AUC, the values within bracket indicate 95% confidence interval with 2000 stratified bootstrap replicas.</p><p>As the window size continues to increase, the rate of increase in scores becomes slow. Increase of scores is ≤ 0.001, as the windows size grows from 23 to 25 for SL477 dataset and ≤ 0.004 for MxD444 dataset, respectively.</p><p>10-fold Cross Validation Performance of DisPredict (Default Parameter).</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590922, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t002", "stats"=>{"downloads"=>9, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_10_fold_Cross_Validation_Performance_of_DisPredict_Default_Parameter_/1590922", "title"=>"10-fold Cross Validation Performance of DisPredict (Default Parameter).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393081"], "description"=>"<p><i>C</i> is the soft penalty parameter to handle overlapped class.</p><p><i>γ</i> is the parameter for radial basis kernel for SVM.</p><p>Optimized Parameters used to build DisPredict Models.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590923, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t003", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimized_Parameters_used_to_build_DisPredict_Models_/1590923", "title"=>"Optimized Parameters used to build DisPredict Models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393082"], "description"=>"<p>W<sub><i>size</i></sub> indicates the size of sliding window and * mark represents window size with overall optimal (best) performance.</p><p>Best values of each metric are marked in bold for each dataset separately.</p><p><sup>1</sup> N<sub><i>correct</i></sub> is reported with total number of residues (Residue<sub><i>total</i></sub>) to be predicted in parentheses. Both of the counts correspond to one subset (fold) of the full dataset which is generated for performing cross validation.</p><p><sup>2</sup> For AUC, the values within bracket indicate 95% confidence interval with 2000 stratified bootstrap replicas.</p><p>10-fold Cross Validation Performance of DisPredict (Optimized Parameter).</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590924, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t004", "stats"=>{"downloads"=>6, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_10_fold_Cross_Validation_Performance_of_DisPredict_Optimized_Parameter_/1590924", "title"=>"10-fold Cross Validation Performance of DisPredict (Optimized Parameter).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393083"], "description"=>"<p>Default values of <i>C</i> and <i>γ</i> are applied for SVM.</p><p>Window size 21 is used.</p><p>DisPredict’s cross validation performance with residue level and sequence level splitting of SL477 dataset.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590925, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t005", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DisPredict_8217_s_cross_validation_performance_with_residue_level_and_sequence_level_splitting_of_SL477_dataset_/1590925", "title"=>"DisPredict’s cross validation performance with residue level and sequence level splitting of SL477 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393084"], "description"=>"<p><sup>1</sup> All the evaluations are carried out using a sliding window of length 21 and optimized parameters for SVM.</p><p>Performance Comparison of Cross Validation and Independent Tests.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590926, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_Comparison_of_Cross_Validation_and_Independent_Tests_/1590926", "title"=>"Performance Comparison of Cross Validation and Independent Tests.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393085"], "description"=>"<p><sup>1</sup> 5-fold cross validation performance of MFDp on MxD dataset of 514 protein chains [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.ref056\" target=\"_blank\">56</a>].</p><p><sup>2</sup> 10-fold cross validation performance of DisPredict on MxD444 which is a subset of 444 chains out of 514 chains with no X-tag.</p><p><sup>3</sup> 10-fold cross validation performance of DisPredict on SL477.</p><p><sup>4</sup> 10-fold cross validation performance of SPINE-D [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.ref047\" target=\"_blank\">47</a>] on SL477.</p><p>Comparative predictive quality of DisPredict with MFDp on MxD444 dataset and SPINE-D on SL477 dataset.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590927, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t007", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparative_predictive_quality_of_DisPredict_with_MFDp_on_MxD444_dataset_and_SPINE_D_on_SL477_dataset_/1590927", "title"=>"Comparative predictive quality of DisPredict with MFDp on MxD444 dataset and SPINE-D on SL477 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393087"], "description"=>"<p>Best results are marked in bold.</p><p>* Window size = 21, <i>C</i> = 2.0 and <i>γ</i> = 0.0078125.</p><p>Performane comparison among DisPredict, SPINE-D and MFDp on independent DD73 dataset.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590929, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.t008", "stats"=>{"downloads"=>7, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performane_comparison_among_DisPredict_SPINE_D_and_MFDp_on_independent_DD73_dataset_/1590929", "title"=>"Performane comparison among DisPredict, SPINE-D and MFDp on independent DD73 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393049"], "description"=>"<p>The x-axis and y-axis show the monograms/bigrams in logarithmic scale and density index of the distribution, respectively. For each figure, the dotted (<i>red</i>) and solid (<i>blue</i>) vertical lines correspond to median values of the distribution for monograms (MG) and bigrams (BG), respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590897, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g001", "stats"=>{"downloads"=>7, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Density_distribution_curves_of_monograms_and_bigrams_for_A_SL477_and_B_MxD444_dataset_/1590897", "title"=>"Density distribution curves of monograms and bigrams for (A) SL477 and (B) MxD444 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393051"], "description"=>"<p>In the feature aggregation step, features are shown in their abbreviated form according to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.t001\" target=\"_blank\">Table 1</a> and the arrows are labeled by the number of features involved. The classification-layer receives final feature set from the feature aggregation step and optimal parameters from the optimization-layer. Then, it generates the predictor model and outputs both binary annotation and real-valued class probabilities.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590899, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g002", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_feature_aggregation_optimization_layer_and_classification_layer_in_DisPredict_/1590899", "title"=>"Overview of feature aggregation, optimization-layer and classification-layer in DisPredict.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393055"], "description"=>"<p>The x-axis and y-axis represent the window sizes and scores, respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590902, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g003", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Increase_of_performance_in_10_fold_cross_validation_default_parameter_according_to_ACC_MCC_and_AUC_scores_with_the_increase_of_window_size_for_A_SL477_and_B_MxD444_dataset_/1590902", "title"=>"Increase of performance in 10-fold cross validation (default parameter) according to ACC, MCC and AUC scores with the increase of window size for (A) SL477 and (B) MxD444 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393061"], "description"=>"<p>In each figure, the solid (<i>blue</i>) curve corresponds to the cross validation test on the same dataset and the dotted (<i>red</i>) curve corresponds to the independent test. The AUC values given in each figure correspond to the values in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.t006\" target=\"_blank\">Table 6</a>. The x-axis and y-axis show the Specificity and Sensitivity, respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590908, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_given_by_DisPredict_for_the_probability_prediction_per_residue_while_the_training_is_performed_with_A_SL477_and_B_MxD444_dataset_/1590908", "title"=>"ROC curves given by DisPredict for the probability prediction per residue while the training is performed with (A) SL477 and (B) MxD444 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/2393064"], "description"=>"<p>The AUC values shown in the figure correspond to the values in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141551#pone.0141551.t008\" target=\"_blank\">Table 8</a>. The x-axis and y-axis show the Specificity and Sensitivity, respectively.</p>", "links"=>[], "tags"=>["RBF kernel", "Optimized RBF Kernel Intrinsically", "case studies", "protein structure", "Novel features", "art approaches", "test datasets", "drug design", "test error", "disorder predictor", "DisPredict yields", "support vector machine", "region", "data sources", "Disordered Protein"], "article_id"=>1590911, "categories"=>["Uncategorised"], "users"=>["Sumaiya Iqbal", "Md Tamjidul Hoque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0141551.g005", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_disorder_prediction_on_DD73_dataset_given_by_DisPredict_blue_SPINE_D_green_and_MFDp_red_/1590911", "title"=>"ROC curves for disorder prediction on DD73 dataset given by DisPredict(<i>blue</i>), SPINE-D(<i>green</i>) and MFDp(<i>red</i>).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-30 04:14:38"}

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  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"6", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"4"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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