Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level
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{"title"=>"Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level", "type"=>"journal", "authors"=>[{"first_name"=>"Kasper", "last_name"=>"Jensen", "scopus_author_id"=>"57201310214"}, {"first_name"=>"Gianni", "last_name"=>"Panagiotou", "scopus_author_id"=>"8566179700"}, {"first_name"=>"Irene", "last_name"=>"Kouskoumvekaki", "scopus_author_id"=>"6602787035"}], "year"=>2014, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84896733774", "doi"=>"10.1371/journal.pcbi.1003432", "pui"=>"372348784", "pmid"=>"24453957", "scopus"=>"2-s2.0-84896733774", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"dc87caa0-5716-3a0b-b0ad-841385d652f6", "abstract"=>"Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research.", "link"=>"http://www.mendeley.com/research/integrated-text-mining-chemoinformatics-analysis-associates-diet-health-benefit-molecular-level", "reader_count"=>70, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>21, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>1, "Student > Master"=>9, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>21, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>1, "Student > Master"=>9, "Student > Bachelor"=>5, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>19, "Arts and Humanities"=>1, "Chemistry"=>2, "Computer Science"=>15, "Earth and Planetary Sciences"=>1, "Engineering"=>4, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>7, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>6, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>1, "Psychology"=>2, "Linguistics"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>2}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>15}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Linguistics"=>{"Linguistics"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}}, "reader_count_by_country"=>{"Canada"=>1, "Czech Republic"=>1, "Netherlands"=>1, "Korea (South)"=>1, "United States"=>2, "Brazil"=>1, "United Kingdom"=>1, "Italy"=>1, "Australia"=>1, "Switzerland"=>2, "Spain"=>2, "India"=>1}, "group_count"=>7}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1349584"], "description"=>"<p>a) Distribution of phytochemicals on the plant space. Rice, soybean, maize and potato are the plants with the most recorded phytochemicals: 4,155, 4,064, 3,361 and 2,988 compounds respectively. b) Structures of representative phytochemicals that have made the way to the pharmacy shelves and their occurrence in respective edible sources.</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "recorded"], "article_id"=>902722, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.g001", "stats"=>{"downloads"=>2, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_plant_species_and_recorded_phytochemicals_/902722", "title"=>"Distribution of plant species and recorded phytochemicals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349585"], "description"=>"<p>a) Examples of well studied foods in relation to positive (disease prevention/amelioration) and negative (disease progression) effect on health. The focus varies from negative effects (below 0) to positive effects (above 0) over the years. The value on the y-axis denotes the number of negative publications subtracted from the number of positive publications in a given year. b) Examples of well studied disease phenotypes in relation to food consumption. Likewise, the figure illustrates a change in focus from negative effects (below 0) to positives effects (above 0).</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "foods", "progression"], "article_id"=>902723, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_association_of_foods_with_disease_prevention_amelioration_or_disease_progression_throughout_time_/902723", "title"=>"The association of foods with disease prevention/amelioration or disease progression throughout time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349586"], "description"=>"<p>a) Disease phenotypes associated with common vegetables, fruits and plants of our diet. Foods are shown as green nodes and human disease phenotypes as purple nodes. Disease prevention/amelioration is depicted as a blue edge and disease promotion as a red edge. The size of the edge indicates the number of publications in support of the association. An edge is drawn between a food node and a disease node when there are at least five publications in support of this association. When a disease node has more than five edges, only the five strongest (with the most publication support) are shown on the network for the sake of clarity. Top left: zoom in the network formed between diabetes mellitus and foods that prevent/ameliorate the disease. Bottom left: zoom in the network formed between Type 1 hypersensitivity and foods that promote it. b) Examples of a vegetable (broccoli), a fruit (blueberry) and a plant-based beverage (camellia-tea) that are only positively associated with disease phenotypes. c) Two examples of foods that are only negatively associated with disease phenotypes.</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition"], "article_id"=>902724, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Food_8211_disease_association_network_/902724", "title"=>"Food – disease association network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349594"], "description"=>"<p>The flow diagram illustrates the approach we followed for associating phytochemicals to human disease phenotypes. a) In the phytochemical - food and food - disease relations that we extracted by text mining, there are 7,077 plants with both phytochemical and human disease annotation. We used Fisher's exact test to identify statistically significant correlations between phytochemical and human disease phenotypes. At a 5% false discovery rate we identified 20,654 phytochemicals associated to 1,592 human disease phenotypes. b) 5,709 of the text-mined phytochemicals have been tested experimentally on a biological target and the activity data have been deposited in ChEMBL. For the remaining two thirds of the compounds, 8,113 phytochemicals are structurally similar to compounds with known protein targets (estimated with a Tanimoto coefficient >0.85), indicating similar bioactivity. The rest of the compounds, 6,832 phytochemicals, are not similar to any known bioactive compound and belong to a hitherto unexplored phytochemical space. c) We used the Therapeutic Targets Database to annotate the protein targets from ChEMBL to diseases. From the 5,709 phytochemicals that are included in ChEMBL, 2,354 are active against a biological target that is relevant for the same disease as the one we have predicted.</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "phytochemicals"], "article_id"=>902726, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.g004", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Association_of_phytochemicals_to_human_disease_phenotypes_/902726", "title"=>"Association of phytochemicals to human disease phenotypes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349596"], "description"=>"<p>a) The KEGG colon cancer disease pathway map is illustrated on the right, where the number of phytochemicals with experimentally measured bioactivity data is depicted as grey ring of varying width. Examples of bioactive phytochemicals are listed on the left, along with typical food source and biological target. b) Protein targets of typical colon cancer drugs and number of phytochemicals with experimental and predicted activities against them. c) From the 6,418 molecules associated with a health benefit for colon cancer, 623 have measured experimental activity against proteins from the colon cancer pathway or targets of colon cancer drugs. On the remaining phytochemical space linked to colon cancer, we can use chemoinformatics to predict activity based on compound structure and select the most promising candidates for <i>in vitro</i> or <i>in vivo</i> experimental validation. Accordingly, we have identified 1,415 phytochemicals with potential activity against colon cancer. For reasons of consistency with the disease pathway map, protein targets are given with their corresponding gene names.</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "colon", "cancer", "pathway"], "article_id"=>902727, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.g005", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Targeting_the_colon_cancer_disease_pathway_with_food_components_/902727", "title"=>"Targeting the colon cancer disease pathway with food components.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349597"], "description"=>"<p>For exemplary cancer types, we list the number of phytochemicals that are similar to small compound drugs that are approved for treatment of the disease (column 3), the number of phytochemicals that have experimental activity against a target implicated in this cancer type (column 4) and the corresponding number of common foods that contain these phytochemicals (column 5).</p><p>DOID: Human Disease Ontology Identifier.</p>1<p>from DRUGBANK.</p>2<p>from ChEMBL and TTD.</p>3<p>similar to a drug (with exp. disease-related target).</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "diseases", "illustrated"], "article_id"=>902728, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.t001", "stats"=>{"downloads"=>6, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phytochemicals_are_associated_with_diseases_via_the_approach_illustrated_in_Figure_4_/902728", "title"=>"Phytochemicals are associated with diseases via the approach illustrated in Figure 4.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349598"], "description"=>"<p>Listed compounds are examples of compounds predicted by our approach to have a positive effect against colon cancer, where p-values are included in column 5.</p>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "foods", "inferred", "colon", "cancer", "chembl"], "article_id"=>902729, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003432.t002", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phytochemicals_column_1_from_common_foods_column_2_with_inferred_activity_to_a_colon_cancer_protein_column_3_based_on_structural_similarity_with_an_active_compound_from_the_ChEMBL_library_column_4_/902729", "title"=>"Phytochemicals (column 1) from common foods (column 2) with inferred activity to a colon cancer protein (column 3), based on structural similarity with an active compound from the ChEMBL library (column 4).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-16 03:34:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1349601", "https://ndownloader.figshare.com/files/1349602"], "description"=>"<div><p>Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research.</p></div>", "links"=>[], "tags"=>["systems biology", "computational chemistry", "medicinal chemistry", "phytochemistry", "Natural Language Processing", "text mining", "nutrition", "mining", "chemoinformatics", "associates", "molecular"], "article_id"=>902732, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Medicine", "Chemistry"], "users"=>["Kasper Jensen", "Gianni Panagiotou", "Irene Kouskoumvekaki"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003432.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003432.s002"], "stats"=>{"downloads"=>7, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integrated_Text_Mining_and_Chemoinformatics_Analysis_Associates_Diet_to_Health_Benefit_at_Molecular_Level_/902732", "title"=>"Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-16 03:34:38"}

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[314, 484]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[285]}, {"subject_area"=>"/Medicine and health sciences/Nutrition", "average_usage"=>[285]}]}
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