Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data
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{"title"=>"Semi-supervised methods to predict patient survival from gene expression data", "type"=>"journal", "authors"=>[{"first_name"=>"Eric", "last_name"=>"Bair", "scopus_author_id"=>"7003465568"}, {"first_name"=>"Robert", "last_name"=>"Tibshirani", "scopus_author_id"=>"7005432426"}], "year"=>2004, "source"=>"PLoS Biology", "identifiers"=>{"scopus"=>"2-s2.0-19344375744", "doi"=>"10.1371/journal.pbio.0020108", "sgr"=>"19344375744", "isbn"=>"1544-9173", "pmid"=>"15094809", "issn"=>"15449173", "pui"=>"39234509"}, "id"=>"bf610661-1f4f-3b8c-8d04-1c32728b90d0", "abstract"=>"An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer.", "link"=>"http://www.mendeley.com/research/semisupervised-methods-predict-patient-survival-gene-expression-data-12", "reader_count"=>204, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>16, "Researcher"=>71, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>55, "Student > Postgraduate"=>6, "Student > Master"=>21, "Other"=>5, "Student > Bachelor"=>10, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>16, "Researcher"=>71, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>55, "Student > Postgraduate"=>6, "Student > Master"=>21, "Other"=>5, "Student > Bachelor"=>10, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>3, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>74, "Chemistry"=>1, "Computer Science"=>40, "Earth and Planetary Sciences"=>1, "Engineering"=>8, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>19, "Nursing and Health Professions"=>1, "Materials Science"=>1, "Mathematics"=>24, "Medicine and Dentistry"=>24, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>24}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>24}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Engineering"=>{"Engineering"=>8}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>74}, "Computer Science"=>{"Computer Science"=>40}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>19}}, "reader_count_by_country"=>{"Hong Kong"=>1, "United States"=>7, "United Kingdom"=>1, "Switzerland"=>1, "Spain"=>2, "Netherlands"=>2, "Belgium"=>3, "Luxembourg"=>1, "Brazil"=>1, "Mexico"=>1, "Italy"=>1, "Australia"=>2, "France"=>1, "Germany"=>1}, "group_count"=>13}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/964579"], "description"=>"<p>Survival Curves for Clusters Derived from the DLBCL Data</p>", "links"=>[], "tags"=>["curves", "clusters", "derived", "dlbcl"], "article_id"=>634509, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Eric Bair", "Robert Tibshirani"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.0020108.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Survival_Curves_for_Clusters_Derived_from_the_DLBCL_Data_/634509", "title"=>"Survival Curves for Clusters Derived from the DLBCL Data", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 09:19:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/964605"], "description"=>"<p>Plot of Survival Versus the Predictor υ̂I for the DLBCL Data</p>", "links"=>[], "tags"=>["predictor", "dlbcl"], "article_id"=>634542, "categories"=>["Cancer", "Biological Sciences", "Genetics"], "users"=>["Eric Bair", "Robert Tibshirani"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.0020108.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Plot_of_Survival_Versus_the_Predictor_965_770_I_for_the_DLBCL_Data_/634542", "title"=>"Plot of Survival Versus the Predictor υ̂I for the DLBCL Data", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-22 09:20:00"}

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Relative Metric

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