From scimedweb from mail.com Thu Apr 2 11:00:58 2009 From: scimedweb from mail.com (scimedweb@mail.com) Date: Thu Apr 2 11:29:26 2009 Subject: [Bioforum] Standard gene nomenclature as defined by HUGO: poor usage in breast cancer marker studies. Message-ID: <618dfca4-9535-48f3-b216-b6b74f4c4eea@s20g2000yqh.googlegroups.com> Poor usage of HUGO standard gene nomenclature in breast cancer studies. by Marc Lacroix InTextoResearch, Baelen, Wallonia, Belgium in Breast Cancer Research and Treatment (2009) 114, 385-386 Since 1989, the Human Genome Organisation (HUGO) attempts to ensure that for each gene there is one name and one symbol. The resulting standard nomenclature is, however, poorly applied in clinical studies, which impairs the efficient retrieval of information. This lack of support is reflected in the present survey of 92 articles reporting on disseminated breast cancer cell detection. Representative markers: KRT19 (Keratin 19), more frequently used aliases: cytokeratin 19; cytokeratin-19; CK19; CK-19; SCGB2A2 (Secretoglobin family 2A, member 2), more frequently used aliases: mammaglobin; mammaglobin A; mammaglobin 1; MAM; mam; hMAM; hMAM-A; MG; MGB1; MMG; SERPINB5 (Serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 5), more frequently used aliases: maspin; MAS; TACSTD1 (Tumor-associated calcium signal transducer 1), more frequently used aliases: epithelial glycoprotein 2; epithelial glycoprotein 40; EGP40; EGP2; KS1/4; GA733-2 From bks from panix.com Thu Apr 2 16:30:06 2009 From: bks from panix.com (Bradley K. Sherman) Date: Thu Apr 2 19:00:20 2009 Subject: [Bioforum] Re: Standard gene nomenclature as defined by HUGO: poor usage in breast cancer marker studies. References: <618dfca4-9535-48f3-b216-b6b74f4c4eea@s20g2000yqh.googlegroups.com> Message-ID: In article <618dfca4-9535-48f3-b216-b6b74f4c4eea@s20g2000yqh.googlegroups.com>, wrote: >in Breast Cancer Research and Treatment (2009) 114, 385-386 > > >Since 1989, the Human Genome Organisation (HUGO) attempts to ensure >that for each gene there is one name and one symbol. The resulting >standard nomenclature is, however, poorly applied in clinical studies, >which impairs the efficient retrieval of information. This lack of >support is reflected in the present survey of 92 articles reporting on >disseminated breast cancer cell detection. > The mistake was in not defining "gene" before proceeding on to enforcing the standard nomenclature for same. --bks From jqb from Cs.Nott.AC.UK Tue Apr 7 10:05:25 2009 From: jqb from Cs.Nott.AC.UK (Jaume Bacardit) Date: Tue Apr 7 10:44:43 2009 Subject: [Bioforum] [Extended Deadline] CFP: Memetic Computing Journal special issue on Metaheuristics for Large Scale Data Mining Message-ID: <49DB6BB5.3010506@cs.nott.ac.uk> [Apologies if you receive multiple times this announcement] Call for Papers: Memetic Computing Journal special issue on Metaheuristics for Large Scale Data Mining - Extended Deadline Guest editors: Jaume Bacardit School of Computer Science and School of Biosciences University of Nottingham jaume.bacardit@nottingham.ac.uk Xavier Llora National Center for Supercomputing Applications University of Illinois at Urbana-Champaign xllora@illinois.edu Submission deadline: May 31st , 2009 Aim and Scope Data mining and knowledge discovery are crucial techniques across many scientific disciplines. Recent developments such as the Genome Project (and its successors) or the construction of the Large Hadron Collider have provided the scientific community with vast amounts of data. Metaheuristics and other evolutionary algorithms have been successfully applied to a large variety of data mining tasks. Competitive metaheuristic approaches are able to deal with rule, tree and prototype induction, neural networks synthesis, fuzzy logic learning, and kernel machines -to mention but a few. Moreover, the inherent parallel nature of some metaheuristics (e.g. evolutionary approaches, particle swarms, ant colonies, etc) makes them perfect candidates for approaching very large-scale data mining problems. Although a number of recent techniques have applied these methods to complex data mining domains, we are still far from having a deep and principled understanding of how to scale them to datasets of terascale, petascale or even larger scale. In order to achieve and maintain a relevant role in large scale data mining, metaheuristics need, among other features, to have the capacity of processing vast amounts of data in a reasonable time frame, to use efficiently the unprecedented computer power available nowadays due to advances in high performance computing and to produce when possible- human understandable outputs. Several research topics impinge on the applicability of metaheuristics for data mining techniques: (1) proper scalable learning paradigms and knowledge representations, (2) better understanding of the relationship between the learning paradigms/representations and the nature of the problems to be solved, (3) efficiency enhancement techniques, and (4) visualization tools that expose as much insight as possible to the domain experts based on the learned knowledge. We would like to invite researchers to submit contributions on the area of large-scale data mining using metaheuristics. Potentially viable research themes are: * Learning paradigms based on metaheuristics, evolutionary algorithms, learning classifier systems, particle swarm, ant colonies, tabu search, simulated annealing, etc * Hybridization with other kinds of machine learning techniques including exact and approximation algorithms * Knowledge representations for large-scale data mining * Advanced techniques for enhanced prediction (classification, regression/function approximation, clustering, etc.) when dealing with large data sets * Efficiency enhancement techniques * Parallelization techniques * Hardware acceleration techniques (vectorial instuctions, GPUs, etc.) * Theoretical models of the scalability limits of the learning paradigms/representations * Principled methodologies for experiment design (choosing methods, adjusting parameters, etc.) * Explanatory power and visualization of generated solutions * Data complexity analysis and measures * Ensemble methods * Online data mining and data streams * Examples of real-world successful applications Instructions for authors Papers should have approximately 20 pages (but certainly not more than 24 pages). The papers must follow the format of the Memetic Computing journal: http://www.springer.com/engineering/journal/12293?detailsPage=contentItemPage&CIPageCounter=151543 Papers should be submitted following the Memetic Computing journal guidelines. When submitting the paper please select this special issue as the article type. Important dates Manuscript submission: May 31st, 2009 Notification of acceptance: July 31st, 2009 Submission of camera-ready version: Sep 30th, 2009 -- ------------------------------------------------------------------- Jaume Bacardit, PhD Lecturer in Bioinformatics University of Nottingham Automated Scheduling, Planning and Optimisation research group, School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB, UK Multidisciplinary Centre for Integrative Biology, School of Biosciences, Sutton Bonington, LE12 5RD, UK Tel: +441159516276 Fax: +44 1159516292 Email: jaume _dot_ bacardit _at_ nottingham _dot_ ac _dot_ uk Web: http://www.cs.nott.ac.uk/~jqb -------------------------------------------------------------------- -- ------------------------------------------------------------------- Jaume Bacardit, PhD Lecturer in Bioinformatics University of Nottingham Automated Scheduling, Planning and Optimisation research group, School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB, UK Multidisciplinary Centre for Integrative Biology, School of Biosciences, Sutton Bonington, LE12 5RD, UK Tel: +441159516276 Fax: +44 1159516292 Email: jaume _dot_ bacardit _at_ nottingham _dot_ ac _dot_ uk Web: http://www.cs.nott.ac.uk/~jqb -------------------------------------------------------------------- This message has been checked for viruses but the contents of an attachment may still contain software viruses, which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation. From qiz from upmc.edu Wed Apr 8 09:11:43 2009 From: qiz from upmc.edu (Qi, Zengbiao) Date: Wed Apr 8 11:38:18 2009 Subject: [Bioforum] transgen expression problem Message-ID: <7F246E57CDE48245AE8CA3DA231CD8BA230528E0@msxmbxnsprd04.acct.upmchs.net> I have developed a transgenic mouse strain expressing HES1 in CD4 positive T cells by utilizing Tet repressor system. The mouse is double positive for M2 (under CD4 promoter) and HES1(under Tet repressor control) and feeding the double positive mice with food containing doxycycline (1g/kg) induces HES1 expression in CD4+ T cells in both thymus and spleens, at least, theoretically. However, HES1 is only induced in thymi, not in spleens of my transgenic mice. Although HES1 can be induced in isolated splenic CD4+ T cells in vitro culture with doxycycline, even without any stimulation in 24 hrs. I’d appreciate your insight of the problem. Zengbiao