Zheng Liu, Tuulikki Sokka, Kevin Maas, Nancy J Olsen, Thomas M Aune
In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified "predictor genes" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 "predictor genes" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.
{"title":"Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling.","authors":"Zheng Liu, Tuulikki Sokka, Kevin Maas, Nancy J Olsen, Thomas M Aune","doi":"10.4061/2009/484351","DOIUrl":"https://doi.org/10.4061/2009/484351","url":null,"abstract":"<p><p>In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified \"predictor genes\" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 \"predictor genes\" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.</p>","PeriodicalId":88887,"journal":{"name":"Human genomics and proteomics : HGP","volume":"2009 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2009-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4061/2009/484351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29352168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jemila S Hamid, Pingzhao Hu, Nicole M Roslin, Vicki Ling, Celia M T Greenwood, Joseph Beyene
Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats, and structures have become available. Among them are gene expression, single nucleotide polymorphism, copy number variation, and protein-protein/gene-gene interactions. Each of these distinct data types provides a different, partly independent and complementary, view of the whole genome. However, understanding functions of genes, proteins, and other aspects of the genome requires more information than provided by each of the datasets. Integrating data from different sources is, therefore, an important part of current research in genomics and proteomics. Data integration also plays important roles in combining clinical, environmental, and demographic data with high-throughput genomic data. Nevertheless, the concept of data integration is not well defined in the literature and it may mean different things to different researchers. In this paper, we first propose a conceptual framework for integrating genetic, genomic, and proteomic data. The framework captures fundamental aspects of data integration and is developed taking the key steps in genetic, genomic, and proteomic data fusion. Secondly, we provide a review of some of the most commonly used current methods and approaches for combining genomic data with focus on the statistical aspects.
{"title":"Data integration in genetics and genomics: methods and challenges.","authors":"Jemila S Hamid, Pingzhao Hu, Nicole M Roslin, Vicki Ling, Celia M T Greenwood, Joseph Beyene","doi":"10.4061/2009/869093","DOIUrl":"https://doi.org/10.4061/2009/869093","url":null,"abstract":"<p><p>Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats, and structures have become available. Among them are gene expression, single nucleotide polymorphism, copy number variation, and protein-protein/gene-gene interactions. Each of these distinct data types provides a different, partly independent and complementary, view of the whole genome. However, understanding functions of genes, proteins, and other aspects of the genome requires more information than provided by each of the datasets. Integrating data from different sources is, therefore, an important part of current research in genomics and proteomics. Data integration also plays important roles in combining clinical, environmental, and demographic data with high-throughput genomic data. Nevertheless, the concept of data integration is not well defined in the literature and it may mean different things to different researchers. In this paper, we first propose a conceptual framework for integrating genetic, genomic, and proteomic data. The framework captures fundamental aspects of data integration and is developed taking the key steps in genetic, genomic, and proteomic data fusion. Secondly, we provide a review of some of the most commonly used current methods and approaches for combining genomic data with focus on the statistical aspects.</p>","PeriodicalId":88887,"journal":{"name":"Human genomics and proteomics : HGP","volume":"2009 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2009-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4061/2009/869093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29352166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01Epub Date: 2008-11-17DOI: 10.4061/2009/597478
George P Patrinos, Emanuel F Petricoin
Human Genomics and Proteomics (HGP) is a new genomics and systems biology journal that is affiliated with an international, open access database. In addition to publishing original research articles and review articles, the journal will also include short descriptions of genetic datasets pertaining to population/ethnic-specific mutation frequencies. HGP is the first scientific journal from SAGE-Hindawi Access to Research, a partnership designed to create a family of open access journals between the publishers SAGE and Hindawi.
{"title":"A new scientific journal linked to a genetic database: towards a novel publication modality.","authors":"George P Patrinos, Emanuel F Petricoin","doi":"10.4061/2009/597478","DOIUrl":"https://doi.org/10.4061/2009/597478","url":null,"abstract":"Human Genomics and Proteomics (HGP) is a new genomics and systems biology journal that is affiliated with an international, open access database. In addition to publishing original research articles and review articles, the journal will also include short descriptions of genetic datasets pertaining to population/ethnic-specific mutation frequencies. HGP is the first scientific journal from SAGE-Hindawi Access to Research, a partnership designed to create a family of open access journals between the publishers SAGE and Hindawi.","PeriodicalId":88887,"journal":{"name":"Human genomics and proteomics : HGP","volume":"2009 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4061/2009/597478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29352167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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