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{"title"=>"Transmembrane topology and signal peptide prediction using dynamic Bayesian networks", "type"=>"journal", "authors"=>[{"first_name"=>"Sheila M.", "last_name"=>"Reynolds", "scopus_author_id"=>"57198164151"}, {"first_name"=>"Lukas", "last_name"=>"Käll", "scopus_author_id"=>"22953585100"}, {"first_name"=>"Michael E.", "last_name"=>"Riffle", "scopus_author_id"=>"6507642434"}, {"first_name"=>"Jeff A.", "last_name"=>"Bilmes", "scopus_author_id"=>"7003705752"}, {"first_name"=>"William Stafford", "last_name"=>"Noble", "scopus_author_id"=>"7102482003"}], "year"=>2008, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"57149112523", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1000213", "pmid"=>"18989393", "pui"=>"352773646", "scopus"=>"2-s2.0-57149112523", "isbn"=>"10.1371/journal.pcbi.1000213"}, "id"=>"71774783-8220-391b-bcec-61face96cd60", "abstract"=>"Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr.", "link"=>"http://www.mendeley.com/research/transmembrane-topology-signal-peptide-prediction-using-dynamic-bayesian-networks", "reader_count"=>101, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>9, "Researcher"=>20, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>4, "Student > Master"=>13, "Other"=>2, "Student > Bachelor"=>14, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>9, "Researcher"=>20, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>4, "Student > Master"=>13, "Other"=>2, "Student > Bachelor"=>14, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>13, "Agricultural and Biological Sciences"=>64, "Medicine and Dentistry"=>3, "Physics and Astronomy"=>1, "Chemistry"=>1, "Psychology"=>1, "Social Sciences"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>64}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Unspecified"=>{"Unspecified"=>6}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Greece"=>1, "Argentina"=>1, "United States"=>2, "Denmark"=>3, "United Kingdom"=>1, "Israel"=>2, "Australia"=>1, "Germany"=>4, "India"=>1}, "group_count"=>4}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/916874"], "description"=>"<p>Overall YRC predictions on 6.3 million proteins: number and relative fraction of each protein type, median protein type confidence score, and median TM topology confidence score (when applicable).</p>", "links"=>[], "tags"=>["yrc", "predictions", "median", "tm", "topology"], "article_id"=>587328, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.t006", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overall_YRC_predictions_on_6_3_million_proteins_number_and_relative_fraction_of_each_protein_type_median_protein_type_confidence_score_and_median_TM_topology_confidence_score_when_applicable_/587328", "title"=>"Overall YRC predictions on 6.3 million proteins: number and relative fraction of each protein type, median protein type confidence score, and median TM topology confidence score (when applicable).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 07:41:35"}
• {"files"=>["https://ndownloader.figshare.com/files/916587"], "description"=>"<p>The total counts and fraction of correct C-terminal localizations as a function of C-terminal segment confidence score for 546 yeast proteins with experimentally assigned C-terminal locations.</p>", "links"=>[], "tags"=>["counts", "c-terminal", "localizations", "546", "yeast", "proteins", "experimentally", "assigned"], "article_id"=>587035, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g008", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_total_counts_and_fraction_of_correct_C_terminal_localizations_as_a_function_of_C_terminal_segment_confidence_score_for_546_yeast_proteins_with_experimentally_assigned_C_terminal_locations_/587035", "title"=>"The total counts and fraction of correct C-terminal localizations as a function of C-terminal segment confidence score for 546 yeast proteins with experimentally assigned C-terminal locations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:39:56"}
• {"files"=>["https://ndownloader.figshare.com/files/916663"], "description"=>"<p>The diagram shows the nine membrane-spanning regions as vertical cylinders, and the cytoplasmic and non-cytoplasmic segments as horizontal bars. Each segment is colored according to type and shaded according to the confidence score. The seventh membrane-helix is missed by many topology predictors and is assigned a relatively low confidence score by Philius and as such is shaded gray. Because of this one low-confidence membrane segment, the location of the C-terminus is less confidently assigned than the location of the N-terminus. On the YRC web page, this diagram is accompanied by the type confidence and topology confidence, as well as a copy of the protein sequence, color coded by segment type. Placing the mouse over any part of the topology diagram or the color-coded sequence will produce a pop-up showing the segment type, confidence, and boundary locations.</p>", "links"=>[], "tags"=>["topology", "presenilin", "shown", "yrc"], "article_id"=>587111, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g009", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Philius_topology_prediction_for_the_human_presenilin_protein_as_shown_on_the_YRC_web_page_/587111", "title"=>"Philius topology prediction for the human presenilin protein as shown on the YRC web-page.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:40:23"}
• {"files"=>["https://ndownloader.figshare.com/files/916293"], "description"=>"<p>Left: precision vs average score for each of the three main protein types. Right: average (black) and average ±one standard deviation (gray) across all proteins.</p>", "links"=>[], "tags"=>["classification", "precision", "computed", "sorting", "proteins", "computing", "sliding"], "article_id"=>586740, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g004", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Protein_type_classification_precision_vs_confidence_score_computed_by_sorting_the_proteins_by_score_and_computing_the_average_score_and_precision_within_a_sliding_window_/586740", "title"=>"Protein-type classification precision vs confidence score computed by sorting the proteins by score and computing the average score and precision within a sliding window.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:38:14"}
• {"files"=>["https://ndownloader.figshare.com/files/916511"], "description"=>"<p>Figure is approximately to scale.</p>", "links"=>[], "tags"=>["phobius", "datasets", "tm", "signalp", "scampi"], "article_id"=>586964, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g007", "stats"=>{"downloads"=>4, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Original_Phobius_datasets_G_SP_G_TM_and_SP_TM_and_new_SignalP_and_SCAMPI_datasets_/586964", "title"=>"Original Phobius datasets (G, SP+G, TM and SP+TM) and new SignalP and SCAMPI datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:39:32"}
• {"files"=>["https://ndownloader.figshare.com/files/916724"], "description"=>"<p>This table shows the fraction of predicted TM and SP+TM that have a single membrane-spanning segment in the mature protein.</p>", "links"=>[], "tags"=>["shows", "tm", "membrane-spanning", "mature"], "article_id"=>587182, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.t008", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_This_table_shows_the_fraction_of_predicted_TM_and_SP_TM_that_have_a_single_membrane_spanning_segment_in_the_mature_protein_/587182", "title"=>"This table shows the fraction of predicted TM and SP+TM that have a single membrane-spanning segment in the mature protein.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 07:40:45"}
• {"files"=>["https://ndownloader.figshare.com/files/916085"], "description"=>"<p>(a) BN with two variables which constitutes the basic (single frame) template for an HMM, and (b) A DBN representation of an HMM obtained by concatenating a variable number of the BN frames and connecting successive state variables.</p>", "links"=>[], "tags"=>["markov"], "article_id"=>586543, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g001", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hidden_Markov_model_/586543", "title"=>"Hidden Markov model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:37:06"}
• {"files"=>["https://ndownloader.figshare.com/files/916130"], "description"=>"<p>(a) Training DBN: only the amino acid and the topoLabel are observed in each frame. The topoLabel is used to constrain the hidden state using an observed child node. The color of the edge between two nodes indicates the type of relationship: black is deterministic, and red is random. (b) First stage decoding DBN: the topoState is hidden and dependent on the state and the previous topoState, and specifies the behavior of </p><p>pType</p>, an additional hidden variable. (c) Second stage decoding DBN: the observed amino acid node and the duration modeling nodes have been removed, and Pr[<i>topoState_i</i>] is defined by the posterior probabilities computed in the first stage using the virtual evidence node topo<i>_VE</i>.<p></p>", "links"=>[], "tags"=>["decoding", "graphical"], "article_id"=>586584, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Philius_training_and_decoding_graphical_models_/586584", "title"=>"Philius training and decoding graphical models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:37:25"}
• {"files"=>["https://ndownloader.figshare.com/files/916767"], "description"=>"<p>The negative set contained 1087 globular proteins.</p>", "links"=>[], "tags"=>["peptide", "matthews", "correlations", "cleavage-site", "sps", "was"], "article_id"=>587214, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.t005", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Philius_signal_peptide_discrimination_accuracy_precision_sensitivity_specificity_and_Matthews_correlations_coefficient_and_cleavage_site_accuracy_fraction_of_all_SPs_detected_for_which_the_cleavage_site_was_predicted_exactly_/587214", "title"=>"Philius signal peptide discrimination (accuracy, precision, sensitivity, specificity, and Matthews correlations coefficient) and cleavage-site accuracy (fraction of all SPs detected for which the cleavage-site was predicted exactly).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 07:40:57"}
• {"files"=>["https://ndownloader.figshare.com/files/916451"], "description"=>"<p>The black line is the average score within the sliding window used to estimate the precision, and the gray lines indicate the average plus and minus one standard deviation.</p>", "links"=>[], "tags"=>["precision", "tm"], "article_id"=>586907, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Full_topology_prediction_precision_vs_score_for_the_TM_proteins_/586907", "title"=>"Full-topology prediction precision vs score for the TM proteins.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:39:13"}
• {"files"=>["https://ndownloader.figshare.com/files/916225"], "description"=>"<p>Each rectangle represents a <i>state</i>, which is characterized by an emission distribution and a duration distribution. The state transition topology of Philius exactly mimics that of Phobius.</p>", "links"=>[], "tags"=>["computational biology/protein structure prediction"], "article_id"=>586683, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_State_transition_diagram_/586683", "title"=>"State transition diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 07:37:57"}
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• {"files"=>["https://ndownloader.figshare.com/files/916937"], "description"=>"<p>Rows are true protein types, and columns are predicted protein types. Where there are two numbers, the first number represents the number of proteins for which the full topology was correctly predicted, while the second number represents the number of proteins for which the protein type was correct but the full topology was not. These results are from the development dataset.</p>", "links"=>[], "tags"=>["matrices", "phobius"], "article_id"=>587391, "categories"=>["Medicine"], "users"=>["Sheila M. Reynolds", "Lukas Käll", "Michael E. Riffle", "Jeff A. Bilmes", "William Stafford Noble"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000213.t003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Confusion_matrices_for_Phobius_and_Philius_/587391", "title"=>"Confusion matrices for Phobius and Philius.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 07:42:00"}

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• {"unique-ip"=>"20", "full-text"=>"17", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
• {"unique-ip"=>"17", "full-text"=>"21", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
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Relative Metric

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F1000Prime