Free basedow s disease software for linux

Aero AIO: Folding@Home is a plugin for Aero AIO to monitor your Folding@Home client. It comes with a simple configuration dialog to set the update interval and client path.

It was inspired by navyrains folding-karamba theme.

Folding@Home is a distributed computing project to understand protein folding, misfolding and related diseases (http://folding.stanford.edu).

ICD Browser for the ICD-10 codes from W.H.O.
This program is an easy to use browser with search capability for the International Classification of Diseases (ICD) codes, as they are published from the W.H.O.
Main features:
- Three versions, for Windows (XP, 2000, 98), Linux and Pocket PC (WM2003, WM5).
- Two ways for displaying codes, Grouped all together into a treebox, or separeted into chapters, sections and subsections.
- Search codes.
- Easy to use interface.

Blossoc is a linkage disequilibrium association mapping tool that attempts to build (perfect) genealogies for each site in the input, score these according to non-random clustering of affected individuals, and judge high-scoring areas as likely candidates for containing disease affecting variation.
Building the local genealogy trees is based on a number of heuristics that are not guaranteed to build true trees, but have the advantage over more sophisticated methods of being extremely fast. Blossoc can therefore handle much larger data sets than more sophisticated tools, but at the cost of sacrificing some accuracy.
Installation:
Blossoc is written in C++ and is available as source code (under the GNU General Public License, GPL) and as binary versions as Linux RPM files. The source code has been successfully compiled on various Linux and UNIX systems. As I have only limited access to architectures other than Linux, it is not possible for me to make binary distributions for other platforms, but if anyone is willing to build the distributions I will be more than happy to put them on this site.
Usage:
Blossoc is started from the command-line and takes, as input, a file containing the positions of markers and a file containing haplotype data.
The format of the haplotype file is: One line per haplotype, where a haplotype is represented as a list of space-separated alleles, and each allele represented as either a 0 or a 1.
The first column is a pseudo-allele used for the case/control dichotomy: a 0 in the first column is taken to mean that the haplotype is a control haplotype and a 1 at the first column is taken to mean that the haplotype is a case haplotype.
Run blossoc --help to get a complete list of command-line options accepted by Blossoc.
Enhancements:
- This version adds support for unphased genotype data through a perfect-phylogeny haplotyping algorithm.
- If enabled, the release will also support the SNPfile binary format.

S-Jog is a GUI for Sony VAIO Jog Wheel.
S-Jog is a program that uses the Sony Vaio laptops Jog Wheel to launch user applications, adjust the screen brightness, volume, and also to scroll pages up and down (like a mousewheel).
S-Jog pops up when you move the Jog Wheel, then disappears after 3 seconds of inactivity so that it doesnt waste desktop space.
The aim of S-Jog is to give Linux a nice graphical application to manage Jog-Dial wheel on Sony Vaio laptops. It utilizes the Sony Programmable I/O driver in recent 2.4.x kernels or the legacy SPIC driver provided in S-Jog distribution.
Currently, S-Jog is able to launch applications defined in the sjogrc file, adjust the screen brightness level and also the master volume of the sound card. Also S-Jog makes the Jog-Dial work like a mousewheel and it runs in background.
Enhancements:
- README, sjog.1: Updated doc file

Statistics::ROC is a Perl module with receiver-operator-characteristic (ROC) curves with nonparametric confidence bounds.

SYNOPSIS

use Statistics::ROC;

my ($y) = loggamma($x);
my ($y) = betain($x, $p, $q, $beta);
my ($y) = Betain($x, $p, $q);
my ($y) = xinbta($p, $q, $beta, $alpha);
my ($y) = Xinbta($p, $q, $alpha);
my (@rk) = rank($type, @r);
my (@ROC) = roc($model_type,$conf,@val_grp);

This program determines the ROC curve and its nonparametric confidence bounds for data categorized into two groups. A ROC curve shows the relationship of probability of false alarm (x-axis) to probability of detection (y-axis) for a certain test. Expressed in medical terms: the probability of a positive test, given no disease to the probability of a positive test, given disease. The ROC curve may be used to determine an optimal cutoff point for the test.
The main function is roc(). The other exported functions are used by roc(), but might be useful for other nonparametric statistical procedures.

loggamma

This procedure evaluates the natural logarithm of gamma(x) for all x>0, accurate to 10 decimal places. Stirlings formula is used for the central polynomial part of the procedure. For x=0 a value of 743.746924740801 will be returned: this is loggamma(9.9999999999E-324).

betain

Computes incomplete beta function ratio

Remarks:
Complete beta function: B(p,q)=gamma(p)*gamma(q)/gamma(p+q)
log(B(p,q))=ln(gamma(p))+ln(gamma(q))-ln(gamma(p+q))

Incomplete beta function ratio:
I_x(p,q)=1/B(p,q) * int_0^x t^{p-1}*(1-t)^{q-1} dt

--> log(B(p,q)) has to be supplied to calculate I_x(p,q)
log denotes the natural logarithm
$beta = log(B(p,q))
$x = x
$p = p
$q = q
The subroutine returns I_x(p,q). If an error occurs a negative value
{-1,-2} is returned.

Betain

Computes the incomplete beta function by calling loggamma() and betain().

xinbta

Computes inverse of incomplete beta function ratio

Remarks:

Complete beta function: B(p,q)=gamma(p)*gamma(q)/gamma(p+q)
log(B(p,q))=ln(gamma(p))+ln(gamma(q))-ln(gamma(p+q))

Incomplete beta function ratio:
alpha = I_x(p,q) = 1/B(p,q) * int_0^x t^{p-1}*(1-t)^{q-1} dt

--> log(B(p,q)) has to be supplied to calculate I_x(p,q)
log denotes the natural logarithm
$beta = log(B(p,q))
$alpha= I_x(p,q)
$p = p
$q = q
The subroutine returns x. If an error occurs a negative value {-1,-2,-3}
is returned.

Xinbta

Computes the inverse of the incomplete beta function by calling loggamma() and xinbta().

rank

Computes the ranks of the values specified as the second argument (an array). Returns a vector of ranks corresponding to the input vector. Different types of ranking are possible (high, low, mean), and are specified as first argument. These differ in the way ties of the input vector, i.e. identical values, are treated:

high:

replace ranks of identical values with their highest rank

low:

replace ranks of identical values with their lowest rank

mean:

replace ranks of identical values with the mean of their ranks

roc

Determines the ROC curve and its nonparametric confidence bounds. The ROC curve shows the relationship of "probability of false alarm" (x-axis) to "probability of detection" (y-axis) for a certain test. Or in medical terms: the "probability of a positive test, given no disease" to the "probability of a positive test, given disease". The ROC curve may be used to determine an "optimal" cutoff point for the test.

The routine takes three arguments:

(1) type of model: decrease or increase, this states the assumption that a higher (increase) value of the data tends to be an indicator of a positive test result or for the model decrease a lower value.
(2) two-sided confidence interval (usually 0.95 is chosen).
(3) the data stored as a list-of-lists: each entry in this list consits of an "value / true group" pair, i.e. value / disease present. Group values are from {0,1}. 0 stands for disease (or signal) not present (prior knowledge) and 1 for disease (or signal) present (prior knowledge). Example: @s=([2, 0], [12.5, 1], [3, 0], [10, 1], [9.5, 0], [9, 1]); Notice the small overlap of the groups. The optimal cutoff point to separate the two groups would be between 9 and 9.5 if the criterion of optimality is to maximize the probability of detection and simultaneously minimize the probability of false alarm.

Returns a list-of-lists with the three curves: @ROC=([@lower_b], [@roc], [@upper_b]) each of the curves is again a list-of-lists with each entry consisting of one (x,y) pair.

Examples:

$,=" ";
print loggamma(10), "n";
print Xinbta(3,4,Betain(.6,3,4)),"n";

@e=(0.7, 0.7, 0.9, 0.6, 1.0, 1.1, 1,.7,.6);
print rank(low,@e),"n";
print rank(high,@e),"n";
print rank(mean,@e),"n";

@var_grp=([1.5,0],[1.4,0],[1.4,0],[1.3,0],[1.2,0],[1,0],[0.8,0],
[1.1,1],[1,1],[1,1],[0.9,1],[0.7,1],[0.7,1],[0.6,1]);
@curves=roc(decrease,0.95,@var_grp);
print "$curves[0][2][0] $curves[0][2][1] n";

K.R.S.S provides a news aggregator for the KDE desktop.
K.R.S.S. is a news aggregator for the KDE desktop. It downloads Rich (RDF) Site Summary files from multiple Web sites and displays them on your desktop in a ticker-tape fashion.
Main features:
- This application is perhaps the only rich site summary display to work on KDE.
- You can downloaded feeds and images locally, and display them as and when you wish.
- You can filter out unwanted news through the use MyFilters, so that you only see the news you want. A feature not found in any other news aggregator.
- You can view feeds either on-line or off-line.
- You can configure a personalized listing of the RSS Feeds, using MyFeeds. You can create and maintain both feeds which you would actively see and feeds which you could just save for future use.
- You can alter the text properties to display the news how you want by using configurable Cascading Style Sheets (CSS). You can start with the template which is provided.
- You can alter the text properties to display the news how you want, using configurable HTML tags ("schemes"). You can also preview instantly what your configuration will look like.
- You can "jump" between feeds, that is, scroll to the top of the feed that you click on, by using MyChannelBar.
- You can control whether or not the feed scrolls when displaying on your desktop, as well as the speed.
- You can define and save an unlimited number of feeds, as well as define which feeds are "active", permitting you to retrieve only the ones you want.
- You can share feeds with other individuals. Tell others of feeds you have discovered, as well as learn which feeds others are using.
- You can take advantage of the pre-loaded set of AmphetaDesks Site Listings - thousands of RSS feed locations, included with K.R.S.S. Or you can import or download the lastest site listings from their website at any time from within K.R.S.S.
- You can configure K.R.S.S. to use the System Tray when you minimize the main window.
- You can get Tips, upon starting up, which help you use K.R.S.S. to the fullest.
- You can set the frequency at which K.R.S.S. will automatically retrieve feeds - in days, hours, or minutes.
- You can temporarily pause or resume scrolling of the feeds.
- You can configure your own shortcut keys, or toolbar menu, or Search button.
- You can change the background and channel title colour, as well as change the spacing between Channels, using the scheming capabilities of MyChannelBar.
- You can DragnDrop RSS feed information using the MyChannelBar feature. Channel Titles and Descriptions are automatically filled in, so you dont have to spend any time typing.
- You can update one feed at a time by using the MyChannlBars popup menu.
- You can set K.R.S.S. to always stay on top of all other windows.
- You can toggle between having active feeds or no active feeds without having to actually specify that the feeds are active or not.
- You can still use K.R.S.S. while you update your feeds.
- You can display date and time information about when a feed was last downloaded and last displayed in the form of a tooltip, by using the Date and Time Tag in MyChannelBar.
- You can set an expiration date on the feeds so that they will expire (that is, become old) after a certain amount of time - in days, hours and minutes.
- You can configure new feeds to display in a different colour by using the Colour Code in MyChannelBar.
Enhancements:
- A fairly recent version of the KDE and QT libraries
- An internet connection

GeneRecon is a software package for linkage disequilibrium mapping using coalescent theory. GeneRecon project is based on a Bayesian Markov-chain Monte Carlo (MCMC) method for fine-scale linkage-disequilibrium gene mapping using high-density marker maps.
GeneRecon explicitly models the genealogy of a sample of the case chromosomes in the vicinity of a disease locus. Given case and control data in the form of genotype or haplotype information, it estimates a number of parameters, most importantly, the disease position.
GeneRecon is written in C++ and Guile Scheme and is available as source code (under the GNU General Public License, GPL) and as binary versions as Linux RPM files.
The source code has been successfully compiled on various Linux and UNIX systems, under OS X and under Windows with Cygwin.
As I have only limited access to architectures other than Linux, it is not possible for me to make binary distributions for other platforms, but if anyone is willing to build the distributions I will be more than happy to put them on this site.
Installation:
$ tar zxf generecon-version.tar.gz
$ cd generecon-version
$ ./configure
$ make
$ make check
$ make install
Usage:
GeneRecon is started from the command-line; the input data and parameters for the analysis are described in one or more configuration scripts written in the Scheme programming language. Starting GeneRecon with the configuration script input.scm is done as:
$ generecon input.scm
Run generecon --help to get a complete list of command-line options accepted by GeneRecon, or see Getting Started for a quick guide to running GeneRecon.
Enhancements:
- This version fixes an incorrect prior on the coalescent tree that resulted in very long convergence times.
- The fix greatly improves both accuracy and number of iterations needed for accurate mapping.