tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - product:"Bioinformatics Toolbox"User-contributed code library2015-03-03T00:21:59-05:001441100tag:www.mathworks.com,2005:FileInfo/495472015-02-05T08:54:29Z2015-02-05T08:54:29Zreal time ecgthis file give real time curve<p>this show real time wave using serial communication</p>Sakib Adnanhttp://www.mathworks.com/matlabcentral/profile/authors/6145544-sakib-adnanMATLAB 7.2 (R2006a)Bioinformatics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/490542015-01-16T09:06:03Z2015-01-16T09:06:03ZPROMPTPRotein cOnformational Motion PredicTion toolbox.<p>The toolbox contains a set of functions to create and process coarse-grained models of protein conformational motion. For more information on the toolbox framework, please check the paper 'Modeling of conformational change by redox-switch modulation of human SSADH' by Gaik Tamazian, Jeong Ho Chang, Sergey Knyazev, Eugene Stepanov, Kyung-Jin Kim and Yuri Porozov, submitted to PLOS ONE.</p>Gaik Tamazianhttp://www.mathworks.com/matlabcentral/profile/authors/3277695-gaik-tamazianMATLAB 8.4 (R2014b)Bioinformatics ToolboxOptimization ToolboxStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/486322014-12-02T19:59:20Z2015-01-14T22:27:59ZMulticlass SVM classifierTrain and perform multiclasses SVM classifier<p>The provided MATLAB functions can be used to train and perform multiclass classification on a data set using a dendrogram-based support vector machine (D-SVM).
<br />The two main functions are:
<br />Train_DSVM: This is the function to be used for training
<br />Classify_DSVM: This is the function to be used for D-SVM classification
<br />Example: Training and classification using fisheriris data
<br />load fisheriris
<br />train_label={zeros(30,1),ones(30,1),2*ones(30,1)};
<br />train_cell={meas(1:30,:),meas(51:80,:),meas(101:130,:)};
<br />[svmstruct] = Train_DSVM(train_cell,train_label);
<br />label=[0 1 2];
<br />test_mat=[meas(31:40,:);meas(81:90,:);meas(131:140,:)];
<br />[Class_test] = Classify_DSVM(test_mat,label,svmstruct);
<br />labels=[zeros(1,10),ones(1,10),2*ones(1,10)];
<br />[Cmat,DA]= confusion_matrix(Class_test,labels,{'A','B','C'});</p>Tarek Lajnefhttp://www.mathworks.com/matlabcentral/profile/authors/2180689-tarek-lajnefMATLAB 7.14 (R2012a)Bioinformatics ToolboxStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/489992015-01-10T04:54:59Z2015-01-10T04:54:59ZGenBank Flat File ReaderRead GenBank-formatted flat file into structure<p>Although the MATLAB Bioinformatics Toolbox has an endogenous GenBank file reader, genbankread(), it sometimes has difficulty reading these flat files with unexpected, but not unorthodox, formatting. This program, gbread(), is designed to replace genbankread() with a more versatile alternative. Unlike genbankread(), which expects fields to maintain strict formatting and order, gbread() blocks off each record as a cell array of text lines, searches for fields, and handles them with a switch/case design. Rather than induce a fatal error, unrecognized fields are appended to the output structure as a block of text. Benchmarks show a surprising ten-fold speed-up of gbread() over genbankread(). Unlike genbankread(), gbread() defaults to automatically parsing features, but does not sacrifice time to reading CDS entries when this option is turned off. Overall, gbread() provides enhanced versatility, reliability, speed, and convenience for reading GenBank flat files in MATLAB.</p>Turner Conradhttp://www.mathworks.com/matlabcentral/profile/authors/5138538-turner-conradMATLAB 8.4 (R2014b)Bioinformatics Toolboxfalsetag:www.mathworks.com,2005:FileInfo/489552015-01-05T22:52:45Z2015-01-05T22:52:45ZOpen Reading Frame Figure GeneratorGenerate open reading frame representation from DNA sequences<p>Although MATLAB has tools to predict open reading frames (ORFs) from nucleotide sequences, none of them directly produce a visual representation of ORFs in a single figure. This program takes a sequence structure as input, predicts ORFs within each sequence, and draws a three-frame (positive only) representation of them. The argument isRef can conveniently transform ORFfigure into a comparison tool whereby the first sequence is used as a reference, local alignment determines the match between each seq entry and the reference, and colors matching sequence as blue and mismatching sequence as red. This software makes it easy to compare how nucleotide variants affect translation of a target gene.</p>Turner Conradhttp://www.mathworks.com/matlabcentral/profile/authors/5138538-turner-conradMATLAB 8.4 (R2014b)Bioinformatics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/487882014-12-18T14:07:04Z2014-12-18T14:08:47ZPlot jdx filesPlots a (selection of) jdx file(s) in a new or specified figure.<p>h=plotjdx(variable,h) Plots a (selection of) jdx file(s) in a new or specified figure. If no file is specified then the function ask for user to select files...
<br />input 'variable' can be:
<br />- a 'filename' of a jdx file,
<br />- a cell with filenames to several jdx files or
<br />- JCAMPStruct to be plotted in
<br />input figure handle 'h' is optional</p>Mickehttp://www.mathworks.com/matlabcentral/profile/authors/1567078-mickeMATLAB 8.4 (R2014b)Bioinformatics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/285302010-08-22T05:05:12Z2014-11-29T12:32:39Zuidendrogram Interactive dendrogram viewer using nested boxes<p>The conventional tree view is a natural way to represent a dendrogram but is only usable for very small trees.
<br />Using the interactive power of computers we can build a more compact view. Leaves are represented as points on a grid. Boxes are drawn round the points to represent the hierarchy. It is possible to set the number of clusters, to select a cluster, or to inspect a leaf. The component is presented with a simple test program (load a fasta file and inspect the pylogeny). </p>Malcolm McLeanhttp://www.mathworks.com/matlabcentral/profile/authors/2210418-malcolm-mcleanMATLAB 7.9 (R2009b)Bioinformatics ToolboxStatistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/477582014-09-05T21:48:43Z2014-11-20T17:39:50ZUncertainty and sensitivity analysis of Simulink models using brute-force methodGlobal sensitivity analysis using the brute-force variance-based method and Monte Carlo simulation<p>Global sensitivity analysis using the variance-based method of "brute force" with combinations of parameters generated by Monte Carlo method with an uniform probability distribution function. Good results are obtained with N > 100. For 4 parameters and 100 sets of parameters in a Intel Core i5 processor the average time of computing is 11 minutes in parallel mode and 20 minutes in normal mode.
<br />Several figures are generated: (1) Plot with temporal responses (Monte Carlo simulation) to all sets of parameters; (2) Scatter plots (parameters and output); (3) Fractional sensitivity indices; (4) Total sensitivity indices; (5) Pie plot with sensitivity indices for every parameter in some time instants</p>
<p>The information about M parameters is introduced as a cell {'Parameter_name', 'Uncertainty_mode', uncertainty_value}. Options: (1) Uncertainty_mode = 'range', uncertainty_value = [min,max]. (2) Uncertainty_mode = 'std', uncertainty_value = [nominal, standard_deviation]. (3) Uncertainty_mode = 'percent', uncertainty_value = [nominal, percent(0,100)].</p>
<p>It is necessary to correctly set the Simulink model: the name of parameters are p(1), p(2),...; connect an "Out block" to the output; in "Configuration parameters | Data Import/Export" check these options: Time, Output, Save simulation output as single object. Use fixed-step solver. Several examples are included. </p>
<p>This is a toolbox in developing (including English translation). Please, send commentaries and suggestions.</p>Carlos M. Velez S.http://www.mathworks.com/matlabcentral/profile/authors/61673-carlos-m-velez-sMATLAB 8.1 (R2013a)Bioinformatics ToolboxSimulinkStatistics ToolboxMATLABIt is possible to use Parallel Computing Toolboxfalsetag:www.mathworks.com,2005:FileInfo/478542014-09-16T20:21:38Z2014-11-12T20:12:40ZApps and models for teaching numerical sciences and computer scienceInteractive apps and Simulink models for numerical sciences and computer science<p>This submission contains a manual and a series of MATLAB Apps and Simulink Models for introducing Numerical Sciences and Computer Science in secondary schools.
<br />It includes the following apps and models :</p>
<p>- Data Type Conversion App. App to convert between boolean, decimal, hexadecimal and character types,</p>
<p>- Simulink model to realize logical operations on booleans,</p>
<p>- JPEG Compression App. This App allows you to change the quality factor of JPEG compression and visualize original, compressed and gray-scale image difference between original and compressed image. </p>
<p>- Watermarking is the process of hiding digital information in a carrier signal, here an image. The Watermarking App allows the user to :
<br /> - Load an image
<br /> - Enter the message to hide
<br /> - Select the bit containing the hidden message
<br /> - Select the color component containing the hidden message (R, G, B)
<br /> - Visualize original image, code image and watermarked image,</p>
<p>- Audio Acquisition App : Interactive acquisition of audio signals,</p>
<p>- Graph Traverse App. This App allows the user to :
<br /> - Load a graph (sparse matrix)
<br /> - Visualize the graph
<br /> - Run a traverse algorithm (DFS or BFS) or find the shortest Path between two nodes
<br /> - Visualize the path, possibly step by step</p>
<p>- Simulink models to communicate an encrypted image between two Raspberry Pi,</p>
<p>- Simulink model for obstacle avoidance on LEGO Mindstorms (using State Machines, stateflow).</p>Emilie Delaherchehttp://www.mathworks.com/matlabcentral/profile/authors/4660227-emilie-delahercheMATLAB 8.4 (R2014b)Bioinformatics ToolboxData Acquisition ToolboxImage Acquisition ToolboxImage Processing ToolboxSimulinkStateflowTarget Support PackageMATLAB44234falsetag:www.mathworks.com,2005:FileInfo/457152014-02-27T19:54:01Z2014-10-07T14:32:30ZQUDeX-MSQUDeX-MS: hydrogen/deuterium exchange calculation for mass spectra with isotopic fine structure<p>Hydrogen-deuterium exchange coupled to mass spectrometry permits analysis of structural dynamics, stability, and molecular interactions of proteins. Resolving isotopic fine structure during mass spectrometry has been recently demonstrated to allow direct detection and quantification of deuterium incorporation distinct from peaks corresponding to non-deuterium incorporated natural abundance heavy isotopomers. Here, we present a graphical tool that allows for a rapid and automated estimation of deuterium incorporation from a spectrum with isotopic fine structure resolved. Given a peptide sequence or elemental formula, the distribution of deuterium-associated mass-to-charge ratios is calculated based on a particular charge state corresponding to the molecular species resolved in an input mass spectrum. Peaks in the input mass spectrum fitting within bins corresponding to these values are used to determine the distribution of deuterium incorporated. A theoretical spectrum can then be calculated within the program based on the estimated distribution of deuterium incorporated to confirm interpretation of the spectrum. Deuterium incorporation can also be detected for ion signals without a priori specification of an elemental formula, permitting detection of exchange in complex samples of unidentified material such as natural organic matter.
<br />Manual available at:
<br /><a href="http://www.agarlabs.com/downloads/QUDeX/QUDeX-MSManualandReleaseNotes.pdf">http://www.agarlabs.com/downloads/QUDeX/QUDeX-MSManualandReleaseNotes.pdf</a></p>Joseph Salisburyhttp://www.mathworks.com/matlabcentral/profile/authors/4501988-joseph-salisburyMATLAB 8.3 (R2014a)Bioinformatics ToolboxStatistics ToolboxMATLABfalse