RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins
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{"title"=>"RNABindRPlus: A predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins", "type"=>"journal", "authors"=>[{"first_name"=>"Rasna R.", "last_name"=>"Walia", "scopus_author_id"=>"55139006200"}, {"first_name"=>"Li C.", "last_name"=>"Xue", "scopus_author_id"=>"43762200800"}, {"first_name"=>"Katherine", "last_name"=>"Wilkins", "scopus_author_id"=>"37027620800"}, {"first_name"=>"Yasser", "last_name"=>"El-Manzalawy", "scopus_author_id"=>"24472631800"}, {"first_name"=>"Drena", "last_name"=>"Dobbs", "scopus_author_id"=>"16678447600"}, {"first_name"=>"Vasant", "last_name"=>"Honavar", "scopus_author_id"=>"7003823702"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84901298266", "doi"=>"10.1371/journal.pone.0097725", "sgr"=>"84901298266", "isbn"=>"1932-6203", "pmid"=>"24846307", "issn"=>"19326203", "pui"=>"373160849"}, "id"=>"761baf78-7df6-316c-9d7e-c9d901bccc6f", "abstract"=>"Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.", "link"=>"http://www.mendeley.com/research/rnabindrplus-predictor-combines-machine-learning-sequence-homologybased-methods-improve-reliability", "reader_count"=>31, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>14, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>1, "Computer Science"=>3, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Japan"=>1, "Brazil"=>1, "United Kingdom"=>1, "Germany"=>1}, "group_count"=>6}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1505121"], "description"=>"<p>The first 6 methods are sequence-based methods. The last 2 methods (indicated by **) are structure-based methods. Methods in each category are sorted in descending order of MCC. The highest value in each column is shown in bold font.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "methods", "rb111"], "article_id"=>1032557, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t011", "stats"=>{"downloads"=>3, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_Methods_on_the_RB111_dataset_/1032557", "title"=>"Evaluation of Methods on the RB111 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505120"], "description"=>"<p>Boundaries of Safe, Twilight, and Dark Zones used by HomPRIP.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "zones"], "article_id"=>1032556, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t012", "stats"=>{"downloads"=>0, "page_views"=>38, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boundaries_of_Safe_Twilight_and_Dark_Zones_used_by_HomPRIP_/1032556", "title"=>"Boundaries of Safe, Twilight, and Dark Zones used by HomPRIP.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505117"], "description"=>"<p>on the RB111 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "rnabindr"], "article_id"=>1032553, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.g005", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_SVMOpt_RNABindRPlus_RNABindR_v2_0_BindN_BindN_and_PPRInt_/1032553", "title"=>"Comparison of SVMOpt, RNABindRPlus, RNABindR v2.0, BindN, BindN+ and PPRInt", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505115"], "description"=>"<p>(A) Actual interface residues, (B) Predictions made by HomPRIP, (C) Predictions made by SVMOpt, and (D) Predictions made by RNABindRPlus.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics"], "article_id"=>1032551, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.g003", "stats"=>{"downloads"=>0, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PDB_ID_3NCU_Chain_A_RIG_I_/1032551", "title"=>"PDB ID: 3NCU, Chain A: RIG-I.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505116"], "description"=>"<p>on the RB44 dataset using (A) ROC curves and (B) PR curves with a 5 Å distance cut-off for interface residues.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics"], "article_id"=>1032552, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_SVMOpt_RNABindRPlus_and_the_Metapredictor_/1032552", "title"=>"Comparison of SVMOpt, RNABindRPlus, and the Metapredictor", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505129"], "description"=>"<p>The first 7 methods are sequence-based methods. The last 2 methods are structure-based methods (indicated by **). Methods in each category are sorted in descending order of MCC. The highest value in each column is shown in bold font.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "methods", "49", "proteins", "rb111"], "article_id"=>1032565, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t007", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_Methods_on_49_proteins_from_the_RB111_dataset_/1032565", "title"=>"Evaluation of Methods on 49 proteins from the RB111 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505114"], "description"=>"<p>Flowchart showing the different components of RNABindRPlus.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics"], "article_id"=>1032550, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_RNABindRPlus_flowchart_/1032550", "title"=>"RNABindRPlus flowchart.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505128"], "description"=>"<p>There are 49 proteins in RB111 for which HomPRIP can find homologs and return predictions.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "homologs"], "article_id"=>1032564, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t006", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proteins_with_Safe_Zone_Homologs_in_RB111_/1032564", "title"=>"Proteins with Safe Zone Homologs in RB111.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505125"], "description"=>"<p>The first 11 methods are sequence-based methods. The last 3 methods are structure-based methods (indicated by **). Methods in each category are sorted in descending order of MCC. The highest value in each column is shown in bold font.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "methods", "28", "proteins", "rb44"], "article_id"=>1032561, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t003", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_Methods_on_28_proteins_from_the_RB44_dataset_/1032561", "title"=>"Evaluation of Methods on 28 proteins from the RB44 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505123"], "description"=>"<p>The Linear Model for Interface Conservation.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "linear", "interface"], "article_id"=>1032559, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t001", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Linear_Model_for_Interface_Conservation_/1032559", "title"=>"The Linear Model for Interface Conservation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505124"], "description"=>"<p>The performance is shown for the Safe, Twilight, and Dark Zones, separately. Prediction coverage is the fraction of queries that can be predicted by HomPRIP in a given zone.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "homprip"], "article_id"=>1032560, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t002", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_HomPRIP_on_RB198_/1032560", "title"=>"Performance of HomPRIP on RB198.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505122"], "description"=>"<p>The first 10 methods are sequence-based methods. The last 3 methods (indicated by **) are structure-based methods. Methods in each category are sorted in descending order of MCC. The highest value in each column is shown in bold font.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "methods", "rb44"], "article_id"=>1032558, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t010", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_Methods_on_the_RB44_dataset_/1032558", "title"=>"Evaluation of Methods on the RB44 dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505119"], "description"=>"<p>Structure-based Methods for Predicting RNA-binding sites in Proteins.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "methods", "predicting", "rna-binding", "sites"], "article_id"=>1032555, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t009", "stats"=>{"downloads"=>0, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structure_based_Methods_for_Predicting_RNA_binding_sites_in_Proteins_/1032555", "title"=>"Structure-based Methods for Predicting RNA-binding sites in Proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505118"], "description"=>"<p>Sequence-based Methods for Predicting RNA-binding sites in Proteins.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "methods", "predicting", "rna-binding", "sites"], "article_id"=>1032554, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t008", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sequence_based_Methods_for_Predicting_RNA_binding_sites_in_Proteins_/1032554", "title"=>"Sequence-based Methods for Predicting RNA-binding sites in Proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505113"], "description"=>"<p>Data points in the plot correspond to the projection of a 6-dimensional vector representing the pairwise alignment of a query and homolog sequence onto a 2-dimensional space defined by the first and second principal components. Blue lines with red circles at their tips represent the axes of the original 6-dimensional space for the 6 variables used in PCA analysis: -log(E) (where is the -value), Identity Score (), Positive Score (), log(L) (where is local alignment length), alignment length fractions ( and , where and are the lengths of the query and homolog proteins, respectively). Each data point is colored according to its computed score, with higher score (red/orange) indicating higher interface conservation and lower scores (blue/green) indicating lower interface conservation. The large gray arrow indicates the direction of increasing degree of interface conservation, from Dark to Twilight to Safe Zone.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "components", "interface", "scores", "alignment"], "article_id"=>1032549, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.g001", "stats"=>{"downloads"=>0, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Principal_Components_Analysis_PCA_of_interface_conservation_scores_and_sequence_alignment_statistics_/1032549", "title"=>"Principal Components Analysis (PCA) of interface conservation scores and sequence alignment statistics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505127"], "description"=>"<p>HomPRIP, RNABindRPlus, and SVMOpt Performance by Zone on RB28.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics", "svmopt"], "article_id"=>1032563, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t005", "stats"=>{"downloads"=>0, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_HomPRIP_RNABindRPlus_and_SVMOpt_Performance_by_Zone_on_RB28_/1032563", "title"=>"HomPRIP, RNABindRPlus, and SVMOpt Performance by Zone on RB28.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1505126"], "description"=>"<p>All measures are highest for proteins with Safe Zone homologs and lowest for those with Dark Zone homologs.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Database and informatics methods", "bioinformatics"], "article_id"=>1032562, "categories"=>["Biological Sciences"], "users"=>["Rasna R. Walia", "Li C. Xue", "Katherine Wilkins", "Yasser EL-Manzalawy", "Drena Dobbs", "Vasant Honavar"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097725.t004", "stats"=>{"downloads"=>0, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_HomPRIP_Performance_by_Zone_on_RB28_/1032562", "title"=>"HomPRIP Performance by Zone on RB28.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-20 03:49:05"}

PMC Usage Stats | Further Information

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Relative Metric

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