An Algorithm for Finding Biologically Significant Features in Microarray Data Based on A Priori Manifold Learning
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{"title"=>"An algorithm for finding biologically significant features in microarray data based on A Priori manifold learning", "type"=>"journal", "authors"=>[{"first_name"=>"Zena M.", "last_name"=>"Hira", "scopus_author_id"=>"56084703300"}, {"first_name"=>"George", "last_name"=>"Trigeorgis", "scopus_author_id"=>"56085715100"}, {"first_name"=>"Duncan F.", "last_name"=>"Gillies", "scopus_author_id"=>"35464353900"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "pmid"=>"24595155", "doi"=>"10.1371/journal.pone.0090562", "pui"=>"372704246", "issn"=>"19326203", "sgr"=>"84896979851", "scopus"=>"2-s2.0-84896979851"}, "id"=>"eb3275b3-43a3-3210-973b-885d57488005", "abstract"=>"Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA) which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed a priori manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process--it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.", "link"=>"http://www.mendeley.com/research/algorithm-finding-biologically-significant-features-microarray-data-based-priori-manifold-learning", "reader_count"=>16, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Student > Master"=>1, "Other"=>1, "Lecturer"=>1, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Student > Master"=>1, "Other"=>1, "Lecturer"=>1, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>1, "Computer Science"=>5, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Iran"=>1, "Japan"=>1, "Slovenia"=>1}, "group_count"=>0}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1405718"], "description"=>"<p>ROC curves found for <i>a priori</i> manifold learning (blue) compared with PCA (Green) and Isomap (Red) computed using the sample-by-sample affinity matrix and the LDA classifier.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "curves", "sample-by-sample", "affinity", "matrices", "linear", "discriminant"], "article_id"=>950414, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g009", "stats"=>{"downloads"=>2, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_sample_by_sample_affinity_matrices_using_Linear_Discriminant_Analysis_/950414", "title"=>"ROC curves for sample-by-sample affinity matrices using Linear Discriminant Analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1405741"], "description"=>"<p>The results show that the variance of the cross validation is very small and thus we can safely compare the methods tested.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "validation", "variance", "gene-by-gene"], "article_id"=>950437, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.t006", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_10_Fold_Cross_Validation_Variance_On_Gene_by_Gene_Transformation_using_k_Nearest_Neighbours_/950437", "title"=>"10 Fold Cross Validation Variance On Gene-by-Gene Transformation using <i>k</i>-Nearest Neighbours.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405708"], "description"=>"<p>The parameter is estimated and the resulting embedding is evaluated using cluster validation and cluster accuracy metrics.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases"], "article_id"=>950404, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_benchmark_/950404", "title"=>"Evaluation benchmark.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405734"], "description"=>"<p>The results show that the variance of the cross validation is very small and thus we can safely compare the methods tested.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "validation", "variance", "gene-by-gene", "linear", "discriminant"], "article_id"=>950430, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.t008", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_10_Fold_Cross_Validation_Variance_On_Gene_by_Gene_Transformation_using_Linear_Discriminant_Analysis_/950430", "title"=>"10 Fold Cross Validation Variance On Gene-by-Gene Transformation using Linear Discriminant Analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405731"], "description"=>"<p>A plot of ROC curves with different percentages of pathways.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "robustness"], "article_id"=>950427, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g018", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_Robustness_Omentum_/950427", "title"=>"Pathway Robustness (Omentum).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405711"], "description"=>"<p>ROC curves found for <i>a priori</i> manifold learning (blue) compared with PCA (Green) and Isomap (Red) computed using the gene-by-gene affinity matrix and the <i>k</i>-NN classifier.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "curves", "gene-by-gene", "affinity", "matrices"], "article_id"=>950407, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g004", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_gene_by_gene_affinity_matrices_using_k_Nearest_Neighbours_/950407", "title"=>"ROC curves for gene-by-gene affinity matrices using <i>k</i>-Nearest Neighbours.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405714"], "description"=>"<p>ROC curves found for <i>a priori</i> manifold learning (blue) compared with PCA (Green) and Isomap (Red) computed using the gene-by-gene affinity matrix and the SVM classifier.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "curves", "gene-by-gene", "affinity", "matrices", "vector"], "article_id"=>950410, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_gene_by_gene_affinity_matrices_using_Support_Vector_Machines_/950410", "title"=>"ROC curves for gene-by-gene affinity matrices using Support Vector Machines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405727"], "description"=>"<p>A plot of ROC curves with different percentages of pathways.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "robustness"], "article_id"=>950423, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g015", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_Robustness_Breast_/950423", "title"=>"Pathway Robustness (Breast).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405740"], "description"=>"<p>The results of 10-fold cross-validation on the dataset using gene-by-gene affinity matrices for PCA and Isomap. The <i>a priori</i> manifold learning method clearly outperforms the other two. We have emphasised in bold the cases which <i>a priori</i> manifold learning outperforms the rest of the methods.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "validation", "gene-by-gene", "linear", "discriminant"], "article_id"=>950436, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.t005", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_10_Fold_Cross_Validation_Accuracy_On_Gene_by_Gene_Transformation_using_Linear_Discriminant_Analysis_/950436", "title"=>"10 Fold Cross Validation Accuracy On Gene-by-Gene Transformation using Linear Discriminant Analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405737"], "description"=>"<p>The results of 10-fold cross-validation on the dataset using sample-by-sample affinity matrices for PCA and Isomap. The <i>a priori</i> manifold learning method (which operates using a gene-by-gene affinity matrix) still provides comparable results with the other methods, while outperforming them in some of the cases. We have emphasised in bold the cases which <i>a priori</i> manifold learning outperforms the rest of the methods.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "validation", "sample-by-sample"], "article_id"=>950433, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.t002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_10_Fold_Cross_Validation_Accuracy_On_Sample_by_Sample_Transformation_using_k_Nearest_Neighbours_/950433", "title"=>"10 Fold Cross Validation Accuracy On Sample-by-Sample Transformation using <i>k</i>-Nearest Neighbours.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405717"], "description"=>"<p>ROC curves found for <i>a priori</i> manifold learning (blue) compared with PCA (Green) and Isomap (Red) computed using the gene-by-gene affinity matrix and the LDA classifier.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "curves", "gene-by-gene", "affinity", "matrices", "linear", "discriminant"], "article_id"=>950413, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g008", "stats"=>{"downloads"=>5, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_for_gene_by_gene_affinity_matrices_using_Linear_Discriminant_Analysis_/950413", "title"=>"ROC curves for gene-by-gene affinity matrices using Linear Discriminant Analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405743", "https://ndownloader.figshare.com/files/1405744", "https://ndownloader.figshare.com/files/1405745", "https://ndownloader.figshare.com/files/1405746", "https://ndownloader.figshare.com/files/1405747", "https://ndownloader.figshare.com/files/1405748", "https://ndownloader.figshare.com/files/1405749"], "description"=>"<div><p>Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA) which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed <i>a priori</i> manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process—it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "algorithm", "biologically", "microarray", "manifold"], "article_id"=>950439, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0090562.s001", "https://dx.doi.org/10.1371/journal.pone.0090562.s002", "https://dx.doi.org/10.1371/journal.pone.0090562.s003", "https://dx.doi.org/10.1371/journal.pone.0090562.s004", "https://dx.doi.org/10.1371/journal.pone.0090562.s005", "https://dx.doi.org/10.1371/journal.pone.0090562.s006", "https://dx.doi.org/10.1371/journal.pone.0090562.s007"], "stats"=>{"downloads"=>7, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/An_Algorithm_for_Finding_Biologically_Significant_Features_in_Microarray_Data_Based_on_A_Priori_Manifold_Learning/950439", "title"=>"An Algorithm for Finding Biologically Significant Features in Microarray Data Based on <i>A Priori</i> Manifold Learning", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405710"], "description"=>"<p>The Dunn Index found using <i>a priori</i> manifold learning learning (Blue) compared with PCA (Green) and Isomap (Red) computed using the gene-by-gene affinity matrix.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "applied", "gene-by-gene", "manifold"], "article_id"=>950406, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dunn_Index_applied_on_gene_by_gene_manifold_for_different_cancers_/950406", "title"=>"Dunn Index applied on gene-by-gene manifold for different cancers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405720"], "description"=>"<p>First the Jaccard coefficient is calculated, the the Mahalanobis distances among the genes and the weights.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "points"], "article_id"=>950416, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g011", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Calculation_of_the_k_Nearest_points_of_the_manifold_/950416", "title"=>"Calculation of the <i>k</i>-Nearest points of the manifold.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405733"], "description"=>"<p>A plot of ROC curves with different percentages of pathways.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "robustness"], "article_id"=>950429, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g019", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_Robustness_Ovary_/950429", "title"=>"Pathway Robustness (Ovary).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1405723"], "description"=>"<p>A plot of the Dunn Index with different percentages of pathways.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome databases", "Genome expression analysis", "microarrays", "algorithms", "Computer applications", "Computer modeling", "Information technology", "databases", "robustness"], "article_id"=>950419, "categories"=>["Biological Sciences"], "users"=>["Zena M. Hira", "George Trigeorgis", "Duncan F. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090562.g013", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_Robustness_Prostate_/950419", "title"=>"Pathway Robustness (Prostate).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-03 03:29:34"}

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