From D.Hardoon from cs.ucl.ac.uk Thu Oct 2 00:51:46 2008 From: D.Hardoon from cs.ucl.ac.uk (David R. Hardoon) Date: Thu Oct 2 09:19:34 2008 Subject: [Computational-biology] Connectionists: Learning from Multiple Sources Workshop, 13 Dec '08 Whistler Canada Message-ID: <1B53D854-D128-4808-95DE-55FDC787C6AD@cs.ucl.ac.uk> Apologies if multiple copies are received. Call for Papers: ---------------------------------------------------------------------- NIPS 2008 WORKSHOP on LEARNING FROM MULTIPLE SOURCES http://web.mac.com/davidrh/LMSworkshop08/ http://nips.cc/ ---------------------------------------------------------------------- IMPORTANT DATES 24 Oct 08 Submission deadline for extended abstracts 28 Oct 08 Notification of acceptance 13 Dec 08 Workshop at NIPS 08, Whistler, Canada BACKGROUND While the machine learning community has primarily focused on analysing output of a single data source, there has been relatively few attempts to develop a general framework, or heuristics, for analysing several data sources in terms of a shared dependency structure. Learning from multiple data sources (or alternatively, the data fusion problem) is a timely research area. Due to the increasing availability and sophistication of data recording techniques and advances in data analysis algorithms, there exists many scenarios in which it is necessary to model multiple, related data sources, i.e. in fields such as bioinformatics, multimodal signal processing, information retrieval etc. The relevance of this research area is inspired by the human brain's ability to integrate five different sensory input streams into a coherent representation of its environment. The open question is to find approaches to analyse data which consists of more than one set of observations (or view) of the same phenomenon. In general, existing methods use a discriminative approach, where a set of features for each data set is found in order to explicitly optimise some dependency criterion. Existing approaches include canonical correlation analysis (Hotelling, 1936), a standard statistical technique for modeling two data sources, and its multiset variation (Kettenring, 1971) which find linearly correlated features between data sets, and kernel variants (Lai and Fyfe, 2000; Bach and Jordan, 2002; Hardoon et al., 2004) and approaches that optimise the mutual information between extracted features (Becker, 1996; Chechik et al., 2003). However, discriminative approaches may be ad hoc, require regularisation to ensure erroneous shared features are not discovered, and it is difficult to incorporate prior knowledge about the shared information. Generative probabilistic approaches address this problem by jointly modeling each data stream as a sum of a shared component and a 'private' component that models the within-set variation (Bach and Jordan, 2005; Leen and Fyfe, 2006; Klami and Kaski, 2006). These approaches assume a simple relationship between (two) data sources, i.e.assuming a so-called 'flat' data structure where the data consists of N independent pairs of related data variables; whereas in practice, related data sources may exhibit extremely complex co- variation (for instance, audio and visual streams related to the same video). A potential solution to this problem could be a fully probabilistic approach, which could be used to impose structured variation within and between data sources. Additional methodological challenges include determining what is the 'useful' information we are trying to learn from the multiple sources and building models for predicting one data source given the others. As well as the unsupervised learning of multiple data sources detailed above, there is the closely related problem of multitask learning (Bickel et al., 2008), or transfer learning, where a task is learned from other related tasks. WORKSHOP The aim of the workshop is to promote discussion amongst leading machine learning and applied researchers about learning from multiple, related sources of data, with a focus on both methodological issues and applied research problems. Topics of the workshop include (but not limited to): - unsupervised learning (generative / discriminative modeling) of multiple related data sources - canonical correlation analysis-type methods - data fusion for real world applications, such as bioinformatics, sensor networks, multimodal signal processing, information retrieval - multitask /transfer learning - multiview learning INVITED SPEAKERS Prof. Michael Jordan University of California, Berkeley http://www.cs.berkeley.edu/~jordan/ Dr. Francis Bach ?cole normale sup?rieure http://www.di.ens.fr/~fbach/ Dr. Tobias Scheffer Max-Planck-Institut fur Informatik http://www.mpi-inf.mpg.de/~scheffer/ ORGANISERS David R. Hardoon (University College London) Gayle Leen (Helsinki University of Technology) Samuel Kaski (Helsinki University of Technology) John Shawe-Taylor (University College London) PROGRAM COMMITTEE Andreas Argyriou (University College London) Tom Dieithe (University College London) Colin Fyfe (University of the West of Scotland) Jaakko Peltonen (Helsinki University of Technology) SUBMISSIONS We invite the submission of high quality extended abstracts (2 to 4 pages) in the NIPS style http://nips.cc/PaperInformation/StyleFiles. Abstracts should be sent (in .pdf/.ps) to D.Hardoon@cs.ucl.ac.uk, gleen@cis.hut.fi . A selection of the submitted abstracts will be accepted as either an oral presentation or poster presentation. The best abstracts will be considered for extended versions in the workshop proceedings, and possibly as a special issue of a journal. From oksayakh from cs.iastate.edu Thu Oct 9 00:01:10 2008 From: oksayakh from cs.iastate.edu (Oksana Yakhnenko) Date: Thu Oct 9 10:51:54 2008 Subject: [Computational-biology] Reminder: NIPS 2008 Workshop on Cost-Sensitive Learning (deadline Oct 17) Message-ID: Apologies if you are receiving multiple copies of this email ==Call for Papers: NIPS 2008 Workshop on Cost-Sensitive Learning== http://www.cs.iastate.edu/~oksayakh/cslworkshop_nips2008.html Description and background ------------------------ The goal of cost-sensitive learning is to minimize data acquisition costs while maximizing the accuracy of the learner/predictor. Many fields in machine learning attempt to solve cost-sensitive learning with strong simplifying assumptions. For example, in semi-supervised learning, class-labels are assumed to be expensive and features are implicitly assumed to have zero cost. In active learning, labels are again assumed to be expensive; however the learner may ask an oracle to reveal a label for unlabeled data for selected examples. Active feature acquisition assumes that obtaining features is expensive (but typically all features are assumed to be equally expensive), and the learner identifies instances for which complete information is most informative to classify a particular test sample. Inductive transfer learning and domain adaptation methods assume that training data for a particular task is expensive or but other data from other domains may be cheaper (although relative costs are usually not explicitly modeled). Cascaded classifier architectures are primarily designed in order to reduce the cost of acquiring features to classify a sample (a sample may be classified the moment the available data is sufficient to provide sufficient classification confidence, without waiting for all features to be obtained). There is an important but neglected common thread linking all of these different research communities. In particular, all these learning methods are motivated by the need to minimize the cost of data acquisition in many different application domains such as computer-aided medical diagnosis, computational linguistics, computational biology, and computer vision. Although all of these areas have felt the need for a principled solution to the problem, the partial solutions that have tried to solve the problem (eg semi-supervised learning, active learning, multi-task inductive transfer etc) rarely model the cost explicitly, and very little effort has been expended on modeling application specific characteristics. Recently to some papers have started modeling the acquisition costs directly, but there is a lot of scope for theoretically rigorous work on this topic. It is also important to explicitly model the requirements from real world application communities and to bridge it with the work on theory/algorithms. Goals --------------- The goal of the workshop is to bring together researchers interested in the application of cost-sensitive learning (computer vision, natural language processing, computer-aided diagnostics, computational biology) with researchers interested in theory & algorithms for learning when data acquisition is costly. The main aim is to focus attention on a practically important problem where practitioners have long sought theoretically sound algorithms but which has not been sufficiently addressed in the literature. A secondary goal is to bring together ideas from semi-supervised learning, active learning, feature acquisition, inductive transfer learning and other areas, in order that there may be more exchange of ideas across these (extremely active) communities. Topics of Interest ------------------------ We welcome both novel theory/algorithms and papers that highlight open problems and challenges in real-world applications which call for cost sensitive learning. Submissions on following topics are particularly encouraged: Algorithms/Theory: -active learning -semi-supervised learning -transfer learning -reinforcement learning -domain adaptation, -cascaded classifier learning -...and related. Applications which call for cost-sensitive learning: -computer vision -computational linguistics -natural language processing -computer-aided diagnosis -differential medical diagnosis -...and others. Paper submission ------------------------ We welcome papers of up to 8 pages in the NIPS 2008 format. The accepted papers will be available for downloading from the workshop website. Accepted papers will be either presented as a talk or poster (with poster spotlight). Papers should be emailed to the organizers at cslworkshop.nips.2008@gmail.com. Please indicate whether you only wish to present a poster. Important Dates ------------------------ Deadline for submissions: October 17, 2008 Notification of acceptance: November 7, 2008 Workshop date: December 13, 2008 Organizers ------------------------ Balaji Krishnapuram (Siemens Medical Solutions USA) Shipeng Yu (Siemens Medical Solutions USA) Oksana Yakhnenko (Iowa State University) R. Bharat Rao (Siemens Medical Solutions USA) Lawrence Carin (Duke University) Invited Speakers ------------------------ John Shawe-Taylor (University College, London) Volker Tresp (University of Munich) Program Committee ------------------------ Chiru Bhattacharya (IISc, Bangalore) Rich Caruana (Cornell) Mario Figueiredo (IST, Portugal) Yves Grandvalet (UTC, France) Yan Liu (IBM) Prem Melville (IBM) Sunita Sarawagi (IIT Bombay) Fei Sha (USC & Yahoo research) Volker Tresp (Siemens) Kai Yu (NEC Research) Ulf Brefeld (Technische Universitaet, Berlin) Steffen Bickel (Max Planck Institute of Computer Science) Vikas Sindhwani (IBM) Johannes F?rnkranz (Darmstadt University) John Shawe-Taylor (University College, London) Sanjoy Dasgupta (University of California, San Diego) Steven Abney (University of Michigan) From almurph from altavista.com Thu Oct 9 05:35:28 2008 From: almurph from altavista.com (almurph@altavista.com) Date: Thu Oct 9 10:52:30 2008 Subject: [Computational-biology] Looping inside Needleman-Wunsch algorithm & good values for the Similairity Matrix Message-ID: <76bd06be-1499-479a-8c61-dced10d6eaae@64g2000hsm.googlegroups.com> Hi everyone, Concerning the Needleman-Wunsch algorithm (cf. http://en.wikipedia.org/wiki/Needleman-Wunsch_algorithm) I have noticed a possible loop. Inside the algorithm there is an important decision making mechanism. Its a "if, else if, else if" structure like: if(ScoreValue == DiagonalValue + SimilarityValue(i, j) { blah; } else if(Score == Left + d) { blah; } else if(Score == Up + d) { blah; } I've been playing with value of the similarity matrix and for certain values I can get the algorithms to stick as there is no else clause to offer an out if none of the above conditions are met. Now I know that I could introuduce an else statement and avoid the loop but it does not address the fundamental probalm of constructing a useful Similarity matrix. It's on this point issue that I want to ask for help. The similaity Matrix that I built for my task is ultimately causing this problem. So i need to make it better. My question is: what properties should a good Similarity matrix have to avoid this loop? How does one go about constructing an effective Similarity matrix? What properties should it have? I'm not referring to the simple structure that score 1 for a match and 0 for a mismatch. i'm referring to more complex structures... Any hints/advice/user-experiences/websites/papers much appreciated. Thanking you, Al. From pDOTpagel from helmholtz-muenchen.de Fri Oct 10 03:10:53 2008 From: pDOTpagel from helmholtz-muenchen.de (Philipp Pagel) Date: Fri Oct 10 08:41:20 2008 Subject: [Computational-biology] Re: Looping inside Needleman-Wunsch algorithm & good values for the Similairity Matrix References: Message-ID: almurph@altavista.com wrote: > Concerning the Needleman-Wunsch algorithm (cf. > http://en.wikipedia.org/wiki/Needleman-Wunsch_algorithm) I have > noticed a possible loop. > Inside the algorithm there is an important decision making mechanism. > Its a "if, else if, else if" structure like: > if(ScoreValue == DiagonalValue + SimilarityValue(i, j) > { > blah; > } > else if(Score == Left + d) > { > blah; > } > else if(Score == Up + d) > { > blah; > } You mean the traceback part - right? > I've been playing with value of the similarity matrix and for certain > values I can get the algorithms to stick as there is no else clause to > offer an out if none of the above conditions are met. By definition, one of these three cases must be true. The reason for that is of course that the dynamic programming matrix was constructed using these rules in the first place. I see two possible reasons for the problem you observe: 1) There is a bug in your program. Either in the construction phase or in the traceback 2) You are experiencing a floating point precision problem. Testing floating point numbers for equality can be problematic... > It's on this point issue that I want to ask for help. The similaity > Matrix that I built for my task is ultimately causing this problem. So > i need to make it better. > My question is: what properties should a > good Similarity matrix have to avoid this loop? How does one go about > constructing an effective Similarity matrix? What properties should it > have? I'm not referring to the simple structure that score 1 for a > match and 0 for a mismatch. i'm referring to more complex > structures... The above seems to indiate that you mean the scoring matrix when you say "similarity matrix" - right?. From the perspective of the algorithm it does not matter at all - you can use whatever scoring matrix you want. Of course from the biological point of view you want to choose a matrix that best matches your problem, question or data. But that is beside the point because no scoring matrix will cause the traceback to fail. cu Philipp -- Dr. Philipp Pagel Lehrstuhl f. Genomorientierte Bioinformatik Technische Universit?t M?nchen http://mips.gsf.de/staff/pagel From oksayakh from cs.iastate.edu Tue Oct 14 22:25:41 2008 From: oksayakh from cs.iastate.edu (Oksana Yakhnenko) Date: Wed Oct 15 09:17:58 2008 Subject: [Computational-biology] (papers due this Firday): NIPS 2008 Workshop on Cost-Sensitive Learning Message-ID: <31A36D22F30E44A2A20A9E4B8A4604F1@OksanaPC> Apologies if you are receiving multiple copies ==Call for Papers: NIPS 2008 Workshop on Cost-Sensitive Learning== http://www.cs.iastate.edu/~oksayakh/cslworkshop_nips2008.html Description and background ------------------------ The goal of cost-sensitive learning is to minimize data acquisition costs while maximizing the accuracy of the learner/predictor. Many fields in machine learning attempt to solve cost-sensitive learning with strong simplifying assumptions. For example, in semi-supervised learning, class-labels are assumed to be expensive and features are implicitly assumed to have zero cost. In active learning, labels are again assumed to be expensive; however the learner may ask an oracle to reveal a label for unlabeled data for selected examples. Active feature acquisition assumes that obtaining features is expensive (but typically all features are assumed to be equally expensive), and the learner identifies instances for which complete information is most informative to classify a particular test sample. Inductive transfer learning and domain adaptation methods assume that training data for a particular task is expensive or but other data from other domains may be cheaper (although relative costs are usually not explicitly modeled). Cascaded classifier architectures are primarily designed in order to reduce the cost of acquiring features to classify a sample (a sample may be classified the moment the available data is sufficient to provide sufficient classification confidence, without waiting for all features to be obtained). There is an important but neglected common thread linking all of these different research communities. In particular, all these learning methods are motivated by the need to minimize the cost of data acquisition in many different application domains such as computer-aided medical diagnosis, computational linguistics, computational biology, and computer vision. Although all of these areas have felt the need for a principled solution to the problem, the partial solutions that have tried to solve the problem (eg semi-supervised learning, active learning, multi-task inductive transfer etc) rarely model the cost explicitly, and very little effort has been expended on modeling application specific characteristics. Recently to some papers have started modeling the acquisition costs directly, but there is a lot of scope for theoretically rigorous work on this topic. It is also important to explicitly model the requirements from real world application communities and to bridge it with the work on theory/algorithms. Goals --------------- The goal of the workshop is to bring together researchers interested in the application of cost-sensitive learning (computer vision, natural language processing, computer-aided diagnostics, computational biology) with researchers interested in theory & algorithms for learning when data acquisition is costly. The main aim is to focus attention on a practically important problem where practitioners have long sought theoretically sound algorithms but which has not been sufficiently addressed in the literature. A secondary goal is to bring together ideas from semi-supervised learning, active learning, feature acquisition, inductive transfer learning and other areas, in order that there may be more exchange of ideas across these (extremely active) communities. Topics of Interest ------------------------ We welcome both novel theory/algorithms and papers that highlight open problems and challenges in real-world applications which call for cost sensitive learning. Submissions on following topics are particularly encouraged: Algorithms/Theory: -active learning -semi-supervised learning -transfer learning -reinforcement learning -domain adaptation, -cascaded classifier learning -...and related. Applications which call for cost-sensitive learning: -computer vision -computational linguistics -natural language processing -computer-aided diagnosis -differential medical diagnosis -...and others. Paper submission ------------------------ We welcome papers of up to 8 pages in the NIPS 2008 format. The accepted papers will be available for downloading from the workshop website. Accepted papers will be either presented as a talk or poster (with poster spotlight). Papers should be emailed to the organizers at cslworkshop.nips.2008@gmail.com. Please indicate whether you only wish to present a poster. Important Dates ------------------------ Deadline for submissions: October 17, 2008 Notification of acceptance: November 7, 2008 Workshop date: December 13, 2008 Organizers ------------------------ Balaji Krishnapuram (Siemens Medical Solutions USA) Shipeng Yu (Siemens Medical Solutions USA) Oksana Yakhnenko (Iowa State University) R. Bharat Rao (Siemens Medical Solutions USA) Lawrence Carin (Duke University) Invited Speakers ------------------------ John Shawe-Taylor (University College, London) Volker Tresp (University of Munich) Program Committee ------------------------ Chiru Bhattacharya (IISc, Bangalore) Rich Caruana (Cornell) Mario Figueiredo (IST, Portugal) Yves Grandvalet (UTC, France) Yan Liu (IBM) Prem Melville (IBM) Sunita Sarawagi (IIT Bombay) Fei Sha (USC & Yahoo research) Volker Tresp (Siemens) Kai Yu (NEC Research) Ulf Brefeld (Technische Universitaet, Berlin) Steffen Bickel (Max Planck Institute of Computer Science) Vikas Sindhwani (IBM) Johannes F?rnkranz (Darmstadt University) John Shawe-Taylor (University College, London) Sanjoy Dasgupta (University of California, San Diego) Steven Abney (University of Michigan) From lawyn from bii.a-star.edu.sg Thu Oct 16 04:18:27 2008 From: lawyn from bii.a-star.edu.sg (lawyn@bii.a-star.edu.sg) Date: Thu Oct 16 12:20:06 2008 Subject: [Computational-biology] ICDM 2008 Travel Grants Message-ID: <55614.172.18.3.132.1224148707.squirrel@webmail.bii-sg.org> ICDM 2008 Travel Grants ======================= Thanks to the NSF (National Science Foundation), the Unesco Privacy Chair, and IBM, ICDM 2008 is able to offer travel grants. Below there is information on eligibility and application requirements for each of these grants. The required documentation should be sent by email, no later than October 24, 2008, to icdm08-grants@isti.cnr.it with subject line: ?ICDM 2008 student travel grant application? in ASCII (highly preferable), or PDF format. Late submissions, or documents in other formats will not be accepted. What is covered --------------- The maximum amount of support provided to each grantee is set by the committee, and it is intended to partially cover the grantee?s lodging and registration. Travel may or may not be partially covered depending on the total availability of funds and the number of awards given. The grantee should pay the registration fee and travel expenses in advance, and then the Grant will reimburse some of these expenses (by check or by bank transfer, depending on the grant). See below for a description of the different travel grants that are available. 1. NSF Sponsored Student Travel Grants -------------------------------------- The purpose of the NSF sponsored student travel grants is to encourage graduate student participation at the conference by partially funding the costs of students who would otherwise be unable to attend. Applications for NSF sponsored grants are accepted only from students at degree granting institutions throughout the USA. The committee strongly prefers to give NSF grants to students who are not paper authors. All students however are encouraged to apply. Other criteria will include evidence of a serious interest in data mining research, as demonstrated by coursework and project experience. ICDM encourages participation of women and under-represented minorities, as well as participation of students from under-represented institutions. 2. Unesco Privacy Chair Sponsored Travel Grants ----------------------------------------------- The UNESCO Chair in Data Privacy (http://unescoprivacychair.urv.cat) offers economic support to offset some of the costs associated with attending ICDM 2008 for participants coming from ?transition? countries, which are nations other than USA, Canada, Western Europe, New Zealand, Australia, Taiwan, Japan and South Korea. Preference is given to student authors, and authors of accepted papers. 3. IBM Sponsored Student Travel Grants -------------------------------------- Thanks to IBM?s sponsorship ICDM is able to offer additional student travel awards. All students are eligible to submit applications. How to apply for a sponsored travel grant ----------------------------------------- A student will have to submit an application that includes: (i) a cv, that shows the institution the applicant is studying/working at, (ii) a letter by the applicant that includes the following: a brief statement of the applicant?s research interest and current accomplishments, and their future research plans, a description of how the grant will help them with their research plans, any information that the student feels relevant for supporting their application, including title of the paper and names and affiliations of the co-authors if the student is an author, (iii) a recommendation letter from the student?s advisor supporting the application, that: confirms that the applicant was a student in good standing at the time the paper was submited to ICDM, describes how the applicant will benefit from attending ICDM, and explains why the student is in need of the travel grant. Non-student applicants should explicitly specify the Unesco Privacy Chair Sponsored Travel Grants in their application, that should include only documents (i) and (ii). The documents should be in ascii (highly recommended) or PDF format. All applications using other formats will be rejected. Send the application with all three required documents in ONE email to: icdm08-grants@isti.cnr.it with subject line: ?ICDM 2008 student travel grant application? by Oct. 24, 2008. From evostar from na.icar.cnr.it Fri Oct 17 08:19:28 2008 From: evostar from na.icar.cnr.it (Evostar 2009) Date: Fri Oct 17 11:22:43 2008 Subject: [Computational-biology] Evostar 2009 - last Call for Papers Message-ID: <48F890E0.5050103@na.icar.cnr.it> * Our apologies if you receive multiple copies of this announcement * --------------------------------------------------------------- L A S T C A L L F O R P A P E R S --------------------------------------------------------------- EVO* 2009 including EuroGP, EvoCOP, EvoBIO and EvoWorkshops 15-17 April, 2009 Eberhard Karls University T?bingen, Germany http://www.evostar.org --------------------------------------------------------------- The EuroGP, EvoCOP and EvoBIO conferences and the workshops collectively entitled EvoWorkshops compose EVO*: Europe's premier co-located events in the field of Evolutionary Computing. Featuring the latest in theoretical and applied research, EVO* topics include recent genetic programming challenges, evolutionary and other meta-heuristic approaches for combinatorial optimization, evolutionary algorithms, machine learning and data mining techniques in the biosciences, in numerical optimization, in music and art domains, in image analysis and signal processing, in hardware optimization and in a wide range of applications to scientific, industrial, financial and other real-world problems. Important dates for all events are: *** Submission deadline: 5 November 2008 *** Notification to authors: 9 January 2009 Camera-ready deadline: 21 January 2009 Conference: 15-17 April 2009 If you are interested in submitting a paper please either go the "Submission" page in the official site http://www.evostar.org, or directly access the event submission pages: http://myreview.csregistry.org/eurogp09/ for EuroGP http://myreview.csregistry.org/evocop09/ for EvoCOP http://myreview.csregistry.org/evobio09/ for EvoBIO http://myreview.csregistry.org/evoworkshops09/ for the EvoWorkshops Scope of the events: EuroGP: Twelfth European Conference on Genetic Programming: high quality papers are sought on topics strongly related to the evolution of computer programs, ranging from theoretical work to innovative applications. EvoCOP: Ninth European Conference on Evolutionary Computation in Combinatorial Optimization: practical and theoretical contributions are invited, related to evolutionary computation techniques and other meta-heuristics for solving combinatorial optimization problems. EvoBIO: Seventh European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: the emphasis is on evolutionary computation and other advanced techniques addressing important problems in molecular biology, proteomics, genomics and genetics, that have been implemented and tested in simulations and on real-life datasets. EvoWorkshops: The twelve workshops which make up this event are focused on the use of Evolutionary Computation in different application areas: EvoCOMNET: Telecommunication networks and other parallel and distributed systems EvoENVIRONMENT: Environmental issues EvoFIN: Finance and economics EvoGAMES: Games EvoHOT: Design automation EvoIASP: Image analysis and signal processing EvoINTERACTION: Interactive evolution and humanized computational intelligence EvoMUSART: Music, sound, art and design EvoNUM: Continuous parameter optimization EvoPHD: Graduate student workshop on evolutionary computation EvoSTOC: Stochastic and dynamic environments (EvoSTOC has organized the ?yellow submarine challenge? competition for dynamic optimization, further information can be found in the workshop page) EvoTRANSLOG: Transportation and logistics In 2009, the event will take place in T?bingen, a traditional university town in Baden-W?rttemberg, Germany, situated on a ridge between the Neckar and Ammer rivers in the southwest of the country, about 30 kms southwest of Stuttgart. EVO* 2009 will be hosted at Eberhard Karls University in T?bingen, founded in 1477 and one of the oldest universities in Germany. Evo* 2009 proceedings will be published by Springer Verlag in the Lecture Notes in Computer Science series. The website http://www.evostar.org offers information relevant to all events, including calls for papers, deadlines, organizing committees, submission requirements, local information and a thorough view on the previous editions. From icouldntbesorrier from gmail.com Fri Oct 17 23:46:41 2008 From: icouldntbesorrier from gmail.com (Iam Sorry) Date: Sat Oct 18 12:38:38 2008 Subject: [Computational-biology] genome decoding Message-ID: <2804d969-1f10-470f-ade8-28e6b9851cb1@64g2000hsu.googlegroups.com> can anybody tell me in a most concise and illustrative manner what exactly 'decoding the human genome' means? I realize that the end result is to identify which genes (segments?) relate to what function in the human body, but what is the process involved for doing so? Links to good articles or just your nutshell understanding of the process is most appreciated! _______________________________________ for the best apology five dollars can buy, visit www.ItsALLMYFuckingFault.com From jprudhomme from healthtech.com Mon Oct 20 13:21:20 2008 From: jprudhomme from healthtech.com (Jim Prudhomme) Date: Mon Oct 20 14:39:30 2008 Subject: [Computational-biology] Call for Speakers: CHI's Structure-Based Drug Design Message-ID: <005801c932e0$a85e5f70$f91b1e50$@com> CALL FOR SPEAKER PROPOSALS Cambridge Healthtech Institute's Ninth Annual STRUCTURE-BASED DRUG DESIGN June 4-5, 2009 | Royal Sonesta Hotel | Cambridge, Massachusetts Submit a speaker proposal at www.healthtech.com/sbd/es.aspx Deadline for submission: November 9, 2008 In this program, we wish to highlight some recent breakthrough stories and successes using SBDD driven by medicinal chemistry, computational and biophysical approaches. We are especially interested in presentations of "unpublished work" that document recent breakthroughs in the following areas: = Case Studies that can be disclosed: "role of SBDD in the identification and development of hit and lead candidates," as told by Medicinal Chemists and Modelers = The "Med Chem" perspective - multi-property optimization and its parallel role with SBDD = Considerations of physicochemical and ADME compound properties (hERG, CYP examples based on X-ray structures - Considerations of in vitro versus in vivo pharmacological compound properties - Compound design: marrying chemical and biological space = Computational approaches - Novel (uses of) computational methodologies: ab initio/QM virtual screening, treatment of protein flexibility, homology models, QSAR, etc. - Virtual screening and docking - Fragment based screening and DeNovo design methods - Pharmacophore modeling = Protein-ligand interactions - Prediction of ligand-target affinity: free energy calculations, MM/PBSA, scoring functions, etc. - Solvation effects with implicit or explicit water models - Can covalent inhibitors bring something new? - Calculations of protein reorganization energy and ligand strain = Role of binding site water molecules - Use of explicit water information in lead optimization - Water contributions to binding specificity = Incorporating kinetic data into structural models - The importance of k_on and k_off; driving efficacy by turning either k_on or k_off - Computational modeling of kinetic behavior = Application of biophysical approaches - Determination of structure of protein-ligand and -protein complexes: X-ray Crystallography and NMR - Determination of protein-ligand affinity: MS, SPR, NMR, ITC = Novel target space: validation and drugability = New directions in SBDD: membrane bound proteins, ion channels, GPCRs All proposals are subject to review by the Scientific Advisory Committee to ensure the overall quality of the conference program. Deadline to submit a speaker proposal is November 9, 2008. For more information, please contact: Micah Lieberman Executive Director, Conferences Cambridge Healthtech Institute 250 First Ave, Suite 300 Needham, MA 02494 Tel: 541-482-4709 E-mail: mlieberman@pharmaseries.com From evostar from na.icar.cnr.it Mon Oct 27 04:06:57 2008 From: evostar from na.icar.cnr.it (Evostar 2009) Date: Mon Oct 27 11:00:55 2008 Subject: [Computational-biology] Evostar 2009 - submission deadline extended Message-ID: <490584B1.9000908@na.icar.cnr.it> *** Evostar 2009 Submission Deadline Extension*** Due to numerous requests, we have decided to extend the submission deadline for all the Evo* events to: 12 November 2008. --------------------------------------------------------------- EVO* 2009 including EuroGP, EvoCOP, EvoBIO and EvoWorkshops 15-17 April, 2009 Eberhard Karls University T?bingen, Germany http://www.evostar.org --------------------------------------------------------------- The EuroGP, EvoCOP and EvoBIO conferences and the workshops collectively entitled EvoWorkshops compose EVO*: Europe's premier co-located events in the field of Evolutionary Computing. Featuring the latest in theoretical and applied research, EVO* topics include recent genetic programming challenges, evolutionary and other meta-heuristic approaches for combinatorial optimization, evolutionary algorithms, machine learning and data mining techniques in the biosciences, in numerical optimization, in music and art domains, in image analysis and signal processing, in hardware optimization and in a wide range of applications to scientific, industrial, financial and other real-world problems. Important dates for all events are: *** Extended Submission deadline: 12 November 2008 *** Notification to authors: 9 January 2009 Camera-ready deadline: 21 January 2009 Conference: 15-17 April 2009 If you are interested in submitting a paper please either go the "Submission" page in the official site http://www.evostar.org, or directly access the event submission pages: http://myreview.csregistry.org/eurogp09/ for EuroGP http://myreview.csregistry.org/evocop09/ for EvoCOP http://myreview.csregistry.org/evobio09/ for EvoBIO http://myreview.csregistry.org/evoworkshops09/ for the EvoWorkshops Scope of the events: EuroGP: Twelfth European Conference on Genetic Programming: high quality papers are sought on topics strongly related to the evolution of computer programs, ranging from theoretical work to innovative applications. EvoCOP: Ninth European Conference on Evolutionary Computation in Combinatorial Optimization: practical and theoretical contributions are invited, related to evolutionary computation techniques and other meta-heuristics for solving combinatorial optimization problems. EvoBIO: Seventh European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: the emphasis is on evolutionary computation and other advanced techniques addressing important problems in molecular biology, proteomics, genomics and genetics, that have been implemented and tested in simulations and on real-life datasets. EvoWorkshops: The twelve workshops which make up this event are focused on the use of Evolutionary Computation in different application areas: EvoCOMNET: Telecommunication networks and other parallel and distributed systems EvoENVIRONMENT: Environmental issues EvoFIN: Finance and economics EvoGAMES: Games EvoHOT: Design automation EvoIASP: Image analysis and signal processing EvoINTERACTION: Interactive evolution and humanized computational intelligence EvoMUSART: Music, sound, art and design EvoNUM: Continuous parameter optimization EvoPHD: Graduate student workshop on evolutionary computation EvoSTOC: Stochastic and dynamic environments (EvoSTOC has organized the "yellow submarine challenge" competition for dynamic optimization, further information can be found in the workshop page) EvoTRANSLOG: Transportation and logistics In 2009, the event will take place in T?bingen, a traditional university town in Baden-W?rttemberg, Germany, situated on a ridge between the Neckar and Ammer rivers in the southwest of the country, about 30 kms southwest of Stuttgart. EVO* 2009 will be hosted at Eberhard Karls University in T?bingen, founded in 1477 and one of the oldest universities in Germany. Evo* 2009 proceedings will be published by Springer Verlag in the Lecture Notes in Computer Science series. The website http://www.evostar.org offers information relevant to all events, including calls for papers, deadlines, organizing committees, submission requirements, local information and a thorough view on the previous editions. From lvannesc from disco.unimib.it Mon Oct 27 04:12:30 2008 From: lvannesc from disco.unimib.it (Vanneschi Leonardo) Date: Mon Oct 27 11:01:01 2008 Subject: [Computational-biology] EuroGP 2009 - submission deadline extended Message-ID: <301E01E95DB8684CAFA9EC28E1032FF207C6FCE2@disco-exchange.disco.local> *** EuroGP 2009 Submission Deadline Extension*** Due to numerous requests, we have decided to extend the submission deadline for EuroGP 2009 to: 12 November 2008 --------------------------------------------------------------------- EuroGP 2009 12th European Conference on Genetic Programming T?bingen, Germany April 15-17 2009 ---------------------------------------------------------------------- EuroGP 2009 will take place in conjunction with EvoCOP 2009 (9th European Conference on Evolutionary Computation in Combinatorial Optimization), EvoBIO 2009 (7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics), and EvoWorkshops 2009 (the specialist workshops on a range of evolutionary computation topics and applications), in a joint event collectively intituled EvoStar 2009. EuroGP 2009 important dates are: *** Extended Submission deadline: 12 November 2008 *** Notification to authors: 9 January 2009 Camera-ready deadline: 21 January 2009 Conference: 15-17 April 2009 To submit a paper you can directly access the page: http://myreview.csregistry.org/eurogp09/ or otherwise find all instructions and information about the EvoStar 2009 events at the page: http://www.evostar.org EuroGP 2009 topics include but are not limited to: * Theoretical developments * Empirical studies of GP performance and behavior * Algorithms, representations and operators * Applications of GP to real-life problems * Hybrid architectures including GP components * Unconventional evolvable computation * Evolutionary design * Evolutionary robotics * Grammar-based GP * Evolvable hardware * Linear GP * Self-reproducing programs * Evolution of tree or graph structures * Evolution of various classes of automata or machines (e.g. cellular automata, finite state machines, pushdown automata, Turing machines) * Object-oriented genetic programming EuroGP 2009 Program Chairs: - Leonardo Vanneschi, University of Milano-Bicocca, Italy vanneschi(at)disco(dot)unimib(dot)it - Steven Gustafson, GE Global Research, USA steven(dot)gustafson(at)research(dot)ge(dot)com From icdm08-publicity from isti.cnr.it Fri Oct 31 11:47:21 2008 From: icdm08-publicity from isti.cnr.it (Maurizio Atzori) Date: Fri Oct 31 13:17:16 2008 Subject: [Computational-biology] IEEE ICDM08: Call for Participation and Early Registration Deadline Message-ID: Call for Participation IEEE International Conference on Data Mining (ICDM 2008) Pisa, Italy, December 15-19, 2008 http://icdm08.isti.cnr.it EARLY REGISTRATION DEADLINE: extended to November 5, 2008. The 2008 edition of the IEEE International Conference on Data Mining series (ICDM 2008) will be held in Pisa, Italy, on December 15 thru 19, 2008. IEEE ICDM is well established as a top ranked research conference in data mining, providing a premier forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. PROGRAM: More information on the program, including a list of accepted papers, is now available at http://icdm08.isti.cnr.it/ . Highlights are: three invited talks by Serge Abiteboul (INRIA Saclay and University Paris-Sud), Ravi Kumar (Yahoo! Inc.), and Harvey J. Miller (University of Utah); four tutorials, a panel on Social Networks and Data Mining, 8 demos, the ICDM contest and 10 workshops. All this in addition to 70 regular papers and 74 short papers accepted out of 724 submissions from 53 countries. VENUE & ACCOMMODATION: The conference venue is located in the historical city center of Pisa. Multiple hotels in all price categories are within walking distance of the venue; see http://icdm08.isti.cnr.it/Accomodation/ for details. TRAVEL: Pisa enjoys excellent accessibility. Pisa Aiport http://www.pisa-airport.com/ is directly reachable from all major European airports and many other airports throughout the world. REGISTRATION: http://icdm08.isti.cnr.it/Registration/ . More information on location, accommodation and travel is available at http://icdm08.isti.cnr.it/ . Hoping to see you in Pisa, Fosca Giannotti and Dimitrios Gunopulos IEEE ICDM 2008 PC Co-chairs Naren Ramakrishnan, Franco Turini, Carlo Zaniolo IEEE ICDM 2008 Conference Co-chairs From tavares from fe.up.pt Wed Oct 15 12:52:20 2008 From: tavares from fe.up.pt (tavares@fe.up.pt) Date: Sat Feb 7 17:12:50 2009 Subject: [Computational-biology] (no subject) Message-ID: ----------------------------------------------------------------------------------------------------------------------------------- (Apologies for cross-posting) International ECCOMAS Thematic Conference VipIMAGE 2009 - II ECCOMAS THEMATIC CONFERENCE ON COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING 21-23th October 2009, FEUP, Porto, Portugal www.fe.up.pt/~vipimage FIRST ANNOUNCE We would appreciate if you could distribute this information by your colleagues and co-workers. ----------------------------------------------------------------------------------------------------------------------------------- Dear Colleague, We are glad to announce the International Conference VipIMAGE 2009 - II ECCOMAS THEMATIC CONFERENCE ON COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING will be held in the Faculty of Engineering of University of Porto, Porto, Portugal, on October 21-23, 2009. Possible Topics (not limited to) • Image Processing and Analysis • Segmentation, Tracking and Analyze of Objects in Images • 3D Vision • Signal Processing • Data Interpolation, Registration, Acquisition and Compression • Objects Simulation • Virtual Reality • Software Development for Image Processing and Analysis • Computer Aided Diagnosis, Surgery, Therapy and Treatment • Computational Bioimaging and Visualization • Telemedicine Systems and their Applications Invited Lecturers • Alejandro Frangi - Pompeu Fabra University, Spain • Christos E. Constantinou - Stanford University School of Medicine, USA • Demetri Terzopoulos - University of California, USA • Joaquim A. Jorge - Instituto Superior Técnico, Portugal • José Carlos Príncipe - University of Florida, USA • Lionel Moisan - Université Paris V, France • Tony Chan - University of California, USA Thematic Sessions Proposals to organize Thematic Session within VipIMAGE 2009 are mostly welcome. The organizers of the selected thematic sessions will be included in the conference scientific committee and will have a reduced registration fee. They will be responsible for the dissemination of their thematic session, may invite expertise researches to have invited keynotes during their session and will participate in the review process of the submitted contributions. Proposals for Thematic Sessions should be submitted by email to the conference co-chairs (tavares@fe.up.pt, rnatal@fe.up.pt) Publications The proceedings book will be published by the Taylor & Francis Group, as happened with VipIMAGE 2007 (ISBN: 9780415457774). The organizers will encourage the submission of extended versions of the accepted papers to related International Journals; in particular for special issues dedicated to the conference. One possibility already confirmed is the International Journal for Computational Vision and Biomechanics (IJCV&B). As what happened with VipIMAGE 2007, the organizers will also propose the publishing of a book by SPRINGER (ISBN: 978-1-4020-9085-1), under the Computational Methods in Applied Sciences series, with invited works from the most important ones presented in conference. Important dates • Deadline for Thematic Sessions proposals: January 15, 2009 • Submission of extended abstracts: March 15, 2009 • Lectures and Final Papers: June 15, 2009 We are looking forward to see you in Porto next year. Kind regards, João Manuel R. S. Tavares Renato Natal Jorge (conference co-chairs) PS. For further details please see the conference website at: www.fe.up.pt/~vipimage