The Experts below are selected from a list of 72825 Experts worldwide ranked by ideXlab platform
Vladimir Brusic - One of the best experts on this subject based on the ideXlab platform.
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MCVdb: A database for knowledge discovery in Merkel cell polyomavirus with applications in T cell immunology and vaccinology
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017Co-Authors: Guang Lan Zhang, James A Decaprio, Lou Chitkushev, Derin B Keskin, Catherine J. Wu, Vladimir BrusicAbstract:Merkel Cell Polyomavirus (MCV) is associated with more than 80% of Merkel cell carcinoma (MCC), a rare but highly lethal form of skin cancer. We made use of the immunological data on MCV available through publications and databases and constructed MCV T cell Antigen Database (MCVdb). MCVdb contains 734 curated antigen entries of MCV antigenic proteins and 30 experimentally verified T cell epitopes. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). A set of computational tools for in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, and T cell epitope conservation analysis have been integrated within the MCVdb. Predicted Class I and Class II HLA-binding peptides for 15 common HLA alleles are included in this database as putative targets. MCVdb is a unique data source providing a comprehensive list of MCV antigens and peptides. MCVdb is publicly available at http://projects.met-hilab.org/mcv/.
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HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology.
Database : the journal of biological databases and curation, 2014Co-Authors: Guang Lan Zhang, Angelika B. Riemer, Ellis L. Reinherz, Lou Chitkushev, Derin B Keskin, Vladimir BrusicAbstract:High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, Classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/.
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hpvdb a data mining system for knowledge discovery in human papillomavirus with applications in t cell immunology and vaccinology
International Conference on Bioinformatics, 2013Co-Authors: Guang Lan Zhang, Angelika B. Riemer, Ellis L. Reinherz, Lou Chitkushev, Derin B Keskin, Vladimir BrusicAbstract:High-risk human papilloma viruses (HPV) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis, and characterization of these cancers, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2865 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. HPVdb also catalogs 96 verified T cell epitopes and 45 verified HLA ligands. Primary amino acid sequences of HPV antigens were collected and annotated from UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). A set of computational tools for in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, Classification of HPV types based on cancer risk, and T cell epitope/HLA ligand visualization, have been integrated in HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a specialized database that integrates curated data and information with tailored analysis tools to facilitate data mining to aid rational vaccine design by discovery of vaccine targets. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of antigen peptides in HPV. It is available at http://cvc.dfci.harvard.edu/hpv/ and http://met-hilab.bu.edu/hpvdb/.
Paul Schimmel - One of the best experts on this subject based on the ideXlab platform.
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functional dissection of a Predicted Class defining motif in a Class ii trna synthetase of unknown structure
Biochemistry, 1994Co-Authors: Matthew W Davis, Douglas D Buechter, Paul SchimmelAbstract:A core of eight beta-strands and three alpha-helices was recently Predicted for the active site domain of Escherichia coli alanyl-tRNA synthetase, an enzyme of unknown structure [Ribas de Pouplana, L1., Buechter, D. D., Davis, M. W., & Schimmel, P. (1993) Protein Sci. 2, 2259-2262; Shi, J.-P., Musier-Forsyth, K., & Schimmel, P. (1994) Biochemistry 26, 5312-5318]. A critical part of this Predicted structure is two antiparallel beta-strands and an intervening loop that make up the second of three highly degenerate sequence motifs that are characteristic of the Class II aminoacyl-tRNA synthetases. We present here an in vivo and in vitro analysis of 21 rationally designed mutations in the Predicted 34-amino acid motif 2 of E. coli alanyl-tRNA synthetase. Although this motif in E. coli alanyl-tRNA synthetase is of a different size than and has only two sequence identities with the analogous motif in yeast aspartyl- and Thermus thermophilus seryl-tRNA synthetases, whose structures are known, the functional consequences of the mutations are explainable in terms of those structures. In particular, the analysis demonstrates the importance of the Predicted motif 2 in adenylate formation, distinguishes between two similar, but distinct, Predicted models for this motif, and distinguishes between the functional importance of two adjacent phenylalanines in a way that strongly supports the Predicted structure. The results suggest that similar analyses will be generally useful in testing models for active site regions of other Class II aminoacyl-tRNA synthetases of unknown structure.
Guang Lan Zhang - One of the best experts on this subject based on the ideXlab platform.
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MCVdb: A database for knowledge discovery in Merkel cell polyomavirus with applications in T cell immunology and vaccinology
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017Co-Authors: Guang Lan Zhang, James A Decaprio, Lou Chitkushev, Derin B Keskin, Catherine J. Wu, Vladimir BrusicAbstract:Merkel Cell Polyomavirus (MCV) is associated with more than 80% of Merkel cell carcinoma (MCC), a rare but highly lethal form of skin cancer. We made use of the immunological data on MCV available through publications and databases and constructed MCV T cell Antigen Database (MCVdb). MCVdb contains 734 curated antigen entries of MCV antigenic proteins and 30 experimentally verified T cell epitopes. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). A set of computational tools for in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, and T cell epitope conservation analysis have been integrated within the MCVdb. Predicted Class I and Class II HLA-binding peptides for 15 common HLA alleles are included in this database as putative targets. MCVdb is a unique data source providing a comprehensive list of MCV antigens and peptides. MCVdb is publicly available at http://projects.met-hilab.org/mcv/.
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HPVdb: a data mining system for knowledge discovery in human papillomavirus with applications in T cell immunology and vaccinology.
Database : the journal of biological databases and curation, 2014Co-Authors: Guang Lan Zhang, Angelika B. Riemer, Ellis L. Reinherz, Lou Chitkushev, Derin B Keskin, Vladimir BrusicAbstract:High-risk human papillomaviruses (HPVs) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis and characterization of these cancers, it is necessary to make full use of the immunological data on HPV available through publications, technical reports and databases. These data vary in granularity, quality and complexity. The extraction of knowledge from the vast amount of immunological data using data mining techniques remains a challenging task. To support integration of data and knowledge in virology and vaccinology, we developed a framework called KB-builder to streamline the development and deployment of web-accessible immunological knowledge systems. The framework consists of seven major functional modules, each facilitating a specific aspect of the knowledgebase construction process. Using KB-builder, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2781 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. The HPVdb also catalogs 191 verified T cell epitopes and 45 verified human leukocyte antigen (HLA) ligands. Primary amino acid sequences of HPV antigens were collected and annotated from the UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature and databases. The data were subject to extensive quality control (redundancy elimination, error detection and vocabulary consolidation). A set of computational tools for an in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, Classification of HPV types based on cancer risk, T cell epitope/HLA ligand visualization, T cell epitope/HLA ligand conservation analysis and sequence variability analysis, has been integrated within the HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a knowledge-based system that integrates curated data and information with tailored analysis tools to facilitate data mining for HPV vaccinology and immunology. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of HPV antigens and peptides. Database URL: http://cvc.dfci.harvard.edu/hpv/.
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hpvdb a data mining system for knowledge discovery in human papillomavirus with applications in t cell immunology and vaccinology
International Conference on Bioinformatics, 2013Co-Authors: Guang Lan Zhang, Angelika B. Riemer, Ellis L. Reinherz, Lou Chitkushev, Derin B Keskin, Vladimir BrusicAbstract:High-risk human papilloma viruses (HPV) are the causes of many cancers, including cervical, anal, vulvar, vaginal, penile and oropharyngeal. To facilitate diagnosis, prognosis, and characterization of these cancers, we constructed the Human Papillomavirus T cell Antigen Database (HPVdb). It contains 2865 curated antigen entries of antigenic proteins derived from 18 genotypes of high-risk HPV and 18 genotypes of low-risk HPV. HPVdb also catalogs 96 verified T cell epitopes and 45 verified HLA ligands. Primary amino acid sequences of HPV antigens were collected and annotated from UniProtKB. T cell epitopes and HLA ligands were collected from data mining of scientific literature. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). A set of computational tools for in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, Classification of HPV types based on cancer risk, and T cell epitope/HLA ligand visualization, have been integrated in HPVdb. Predicted Class I and Class II HLA binding peptides for 15 common HLA alleles are included in this database as putative targets. HPVdb is a specialized database that integrates curated data and information with tailored analysis tools to facilitate data mining to aid rational vaccine design by discovery of vaccine targets. To our best knowledge, HPVdb is a unique data source providing a comprehensive list of antigen peptides in HPV. It is available at http://cvc.dfci.harvard.edu/hpv/ and http://met-hilab.bu.edu/hpvdb/.
Jeremy T. Goldbach - One of the best experts on this subject based on the ideXlab platform.
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Gender and Sexual Identities Predicting Patterns of Co-occurring Health Risks Among Sexual Minority Youth: a Latent Class Analysis Approach
Prevention Science, 2020Co-Authors: Ankur Srivastava, Jordan P. Davis, Jeremy T. GoldbachAbstract:Behavioral health disparities (e.g., substance use, mental health) exist for gender nonconforming (GNC) and sexual minority youth; however, there is limited knowledge on disparities that may be unique among youth who identify as both a sexual and gender minority. This study utilized a diverse sample of GNC and cisgender sexual minority youth seeking crisis services to examine co-occurrence of behavioral health outcomes. Surveys were administered with youth (aged 12–24, N = 592), and latent Class analyses were applied. Two latent Class regression models were conducted to examine how gender and sexual identity separately (independent effect; Model 1) and configurations of gender and sexual identity (Model 2) Predicted Class membership. Analyses resulted in a four-Class solution: High All (17.6%), High Substance Use and Moderate Mental Health (10.6%), Low All (20.1%), and High Suicide and High Mental Health (51.7%). In our first model, youth who identified as GNC had 2.11 higher odds of being in the High Suicide and High Mental Health Class compared to the Low All Class; however, sexual identity was not a significant predictor. In the second model, individuals identifying as GNC gay or lesbian or GNC pansexual had 1.95 and 2.57 higher odds, respectively, of being in the High Suicide and High Mental Health Class compared to the Low All Class. Our study suggests the information on gender and sexual identities together are more helpful in identifying youth at risk for co-occurring negative health outcomes. Implications for prevention approaches are described.
John E. Pachankis - One of the best experts on this subject based on the ideXlab platform.
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syndemic profiles and sexual minority men s hiv risk behavior a latent Class analysis
Archives of Sexual Behavior, 2021Co-Authors: Jillian R Scheer, Kirsty A Clark, Anthony J Maiolatesi, John E. PachankisAbstract:Syndemic theory posits that “syndemic conditions” (e.g., alcohol misuse, polydrug use, suicidality) co-occur among sexual minority men and influence HIV-risk behavior, namely HIV acquisition and transmission risk. To examine how four syndemic conditions cluster among sexual minority men and contribute to HIV-risk behavior, we conducted latent Class analysis (LCA) to: (1) Classify sexual minority men (n = 937) into subgroups based on their probability of experiencing each syndemic condition; (2) examine the demographic (e.g., race/ethnicity) and social status (e.g., level of socioeconomic distress) characteristics of the most optimally fitting four syndemic Classes; (3) examine between-group differences in HIV-risk behavior across Classes; and (4) use syndemic Class membership to predict HIV-risk behavior with sexual minority men reporting no syndemic conditions as the reference group. The four Classes were: (1) no syndemic, (2) alcohol misuse and polydrug use syndemic, (3) polydrug use and HIV syndemic, and (4) alcohol misuse. HIV-risk behavior differed across these latent Classes. Demographic and social status characteristics Predicted Class membership, suggesting that syndemic conditions disproportionately co-occur in vulnerable subpopulations of sexual minority men, such as those experiencing high socioeconomic distress. When predicting HIV-risk behavior, men in the polydrug use and HIV syndemic Class were more likely (Adjusted Risk Ratio [ARR] = 2.93, 95% CI: 1.05, 8.21) and men in the alcohol misuse Class were less likely (ARR = 0.17, 95% CI: 0.07, 0.44) to report HIV-risk behavior than were men in the no syndemic Class. LCA represents a promising methodology to inform the development and delivery of tailored interventions targeting distinct combinations of syndemic conditions to reduce sexual minority men’s HIV-risk behavior.
