http://www.bibsonomy.org/burst/concept/tag/bioBibSonomy BuRST Feed for /concept/tag/bio2008-07-21T00:27:31+02:00http://www.bibsonomy.org/bibtex/2f42c5f51c4931c8a527fdead9b359594/guning1guning12008-04-15T10:02:46+02:00Bio <span style="color:#555555;"> </span><em>Bioinformatics and Biomedical Engineering</em>(<em>2007</em>)Tue Apr 15 10:02:46 CEST 2008Bioinformatics and Biomedical EngineeringAn Adaptive Wavelet Method for Clutter Rejection of Doppler Signal: A Simulation Study2007Bio An adaptive method combining wavelet transform (WT) with the bivariate shrinkage function (BSF) with local variance estimation is presented for rejecting the strong clutter from Doppler ultrasound signal. The raw Doppler signal is decomposed by the undecimated WT in which the fast algorithm of translation invariant discrete wavelet transform (TIDWT) is adopted, and then the scale and wavelet coefficients selected in terms of clutter frequency range are shrunk by BSF. The clutter is first reconstructed by updated coefficients. This new method was validated by simulated carotid Doppler signal, and compared to static high pass filter (HPF). The improvements including spectrogram and signal-to-clutter (S/C) ratio by the new method over HPF was noticeable. For the simulated signal with S/C ratio -20 dB, the improvement in S/C ratio was 22.66 by the new method, but 22.05 by HPF. The mean spectrum further indicated that the new method effectively reject clutter and simultaneously possess nicer retention of blood components.http://www.bibsonomy.org/bibtex/2f8513438dd2fb9719724f8f8014380e2/guning1guning12008-04-15T09:54:32+02:00Bio <span style="color:#555555;"> </span>(<em>2006</em>)Tue Apr 15 09:54:32 CEST 2008] Purification and unique properties of mammary epithelial stem cells2006Bio http://www.bibsonomy.org/bibtex/2e6038de64c4ef310c493516a71e56eb8/alpealpe2008-01-30T16:42:10+01:00shearbib bio <span style="color:#555555;">S. N. <a href="http://www.bibsonomy.org/author/Patek">Patek</a> and J. E. <a href="http://www.bibsonomy.org/author/Baio">Baio</a> </span><em>J. Exp Biol.2007</em>(<em>2007</em>)Wed Jan 30 16:42:10 CET 2008J. Exp Biol.20073538-3546 The acoustic mechanics of stick–slip friction in the California spiny lobster (Panulirus interruptus)210:2007shearbib bio The dynamic interplay between static and sliding friction is fundamental to many animal movements. One interesting example of stick–slip friction is found in the sound-producing apparatus of many spiny lobster species (Palinuridae). The acoustic movements of the spiny lobster's plectrum over the file are generated by stick–slip friction between the two surfaces. We examined the microscopic anatomy, kinematics, acoustics and frictional properties of the California spiny lobster (Panulirus interruptus) toward the goal of quantitatively characterizing the frictional and acoustic mechanics of this system. Using synchronous high-speed video and sound recordings, we tested whether plectrum kinematics are correlated with acoustic signal features and found that plectrum velocity is positively correlated with acoustic amplitude. To characterize the frictional mechanics of the system, we measured frictional forces during sound production using excised plectrums and files. Similar to rubber materials sliding against hard surfaces, the static coefficient of friction in this system was on average 1.7. The change in the coefficient of friction across each stick–slip cycle varied substantially with an average change of 1.1. Although driven at a constant speed, the plectrum slipped at velocities that were positively correlated with the normal force between the two surfaces. Studies of friction in biological systems have focused primarily on adhesion and movement, while studies of stick–slip acoustics have remained under the purview of musical acoustics and engineering design. The present study offers an integrative analysis of an unusual bioacoustic mechanism and contrasts its physical parameters with other biological and engineered systems.Bayesian methods in bioinformatics and computational systems biology -- Wilkinson 8 (2): 109 -- Briefings in Bioinformaticshttp://www.bibsonomy.org/bibtex/297cacebfa75cfaadcbfec9b2fd4e57fe/wnpxrzwnpxrz2008-01-12T12:28:46+01:00bayesian proj:o4p bio imported toread <span style="color:#555555;">Darren J. <a href="http://www.bibsonomy.org/author/Wilkinson">Wilkinson</a> </span><em>Brief Bioinform</em><em>8(2):109-116</em>(<em>2007</em>)Sat Jan 12 12:28:46 CET 2008Brief Bioinform2109-116{Bayesian methods in bioinformatics and computational systems biology}82007bayesian proj:o4p bio imported toread Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology. CiteULike: A Primer on Learning in Bayesian Networks for Computational Biologyhttp://www.bibsonomy.org/bibtex/21dc28e845cb66e9ce61858c1b06c9c0c/wnpxrzwnpxrz2008-01-12T12:18:34+01:00bio toread proj:o4p bayesian <span style="color:#555555;">Chris J. <a href="http://www.bibsonomy.org/author/Needham">Needham</a> and James R. <a href="http://www.bibsonomy.org/author/Bradford">Bradford</a> and Andrew J. <a href="http://www.bibsonomy.org/author/Bulpitt">Bulpitt</a> and David R. <a href="http://www.bibsonomy.org/author/Westhead">Westhead</a> </span><em>PLoS Computational Biology</em><em>3(8):e129+</em><em>August2007. </em>Sat Jan 12 12:18:34 CET 2008PLoS Computational BiologyAugust8e129+A Primer on Learning in Bayesian Networks for Computational Biology32007bio toread proj:o4p bayesian http://www.bibsonomy.org/bibtex/20491ed69eed20f21fda871e5990cce80/seandalaiseandalai2007-11-07T19:43:15+01:002007 kernels featureselection bio <span style="color:#555555;">Le <a href="http://www.bibsonomy.org/author/Song">Song</a> and Justin <a href="http://www.bibsonomy.org/author/Bedo">Bedo</a> and Karsten M. <a href="http://www.bibsonomy.org/author/Borgwardt">Borgwardt</a> and Arthur <a href="http://www.bibsonomy.org/author/Gretton">Gretton</a> and Alex <a href="http://www.bibsonomy.org/author/Smola">Smola</a> </span><em>Bioinformatics</em><em>23(13):490--498</em>(<em>2007</em>)Wed Nov 07 19:43:15 CET 2007Bioinformatics13490--498Gene selection via the BAHSIC family of algorithms2320072007 kernels featureselection bio Motivation: Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical connections and the differences between these methods. In this article, we define a kernel-based framework for feature selection based on the HilbertSchmidt independence criterion and backward elimination, called BAHSIC. We show that several well-known feature selectors are instances of BAHSIC, thereby clarifying their relationship. Furthermore, by choosing a different kernel, BAHSIC allows us to easily define novel feature selection algorithms. As a further advantage, feature selection via BAHSIC works directly on multiclass problems. Results: In a broad experimental evaluation, the members of the BAHSIC family reach high levels of accuracy and robustness when compared to other feature selection techniques. Experiments show that features selected with a linear kernel provide the best classification performance in general, but if strong non-linearities are present in the data then non-linear kernels can be more suitable. Availability: Accompanying homepage is http://www.dbs.ifi.lmu.de/~borgward/BAHSIC Contact: kb@dbs.ifi.lmu.de Supplementary information: Supplementary data are available at Bioinformatics online. http://www.bibsonomy.org/bibtex/29ff8a52627cd0767fbda7eb069f3f0b6/iccsiccs2007-08-02T09:29:45+02:00analysis iccs fca informatics 2005 formal bio concept <span style="color:#555555;">Nizar <a href="http://www.bibsonomy.org/author/Messai">Messai</a> and Marie-Dominique <a href="http://www.bibsonomy.org/author/Devignes">Devignes</a> and Amedeo <a href="http://www.bibsonomy.org/author/Napoli">Napoli</a> and Malika <a href="http://www.bibsonomy.org/author/Sma\&#034;{\i}l-Tabbone">Sma&quot;il-Tabbone</a> </span><em>Proceedings of the 13th International Conference on Conceptual Structures (ICCS 2005), </em><em>volume3596ofLecture Notes in Computer Science, </em><em>page323-336. </em><em>Springer, </em>(<em>2005</em>)Thu Aug 02 09:29:45 CEST 2007Proceedings of the 13th International Conference on Conceptual Structures (ICCS 2005)DBLP:conf/iccs/2005323-336Lecture Notes in Computer ScienceQuerying a Bioinformatic Data Sources Registry with Concept Lattices.35962005analysis iccs fca informatics 2005 formal bio concept