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----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

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GitHub - seeslab/drugraph: A network inference method for large-scale unsupervised identification of novel drug-drug interactions. · GitHub
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----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

1 star

Watchers

2 watching

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----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

1 star

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - seeslab/drugraph: A network inference method for large-scale unsupervised identification of novel drug-drug interactions. · GitHub
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Repository files navigation

----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

1 star

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' GitHub - seeslab/drugraph: A network inference method for large-scale unsupervised identification of novel drug-drug interactions. · GitHub
Skip to content

Repository files navigation

----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

1 star

Watchers

2 watching

Forks

Releases

Packages

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - seeslab/drugraph: A network inference method for large-scale unsupervised identification of novel drug-drug interactions. · GitHub
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----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

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Stars

1 star

Watchers

2 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - seeslab/drugraph: A network inference method for large-scale unsupervised identification of novel drug-drug interactions. · GitHub
Skip to content

Repository files navigation

----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

1 star

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); GitHub - seeslab/drugraph: A network inference method for large-scale unsupervised identification of novel drug-drug interactions. · GitHub
Skip to content

Repository files navigation

----------------------------------------------------------------------
----------------------------------------------------------------------
DRUGraph README
Last changed: 2013/09/30
----------------------------------------------------------------------
----------------------------------------------------------------------
This package includes the source code for the network algorithm for
the identification of drug-drug interactions (REF).
For the code to compile, you will NEED TO INSTALL, first:
1) The GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/
----------------------------------------------------------------------
INSTALLATION
----------------------------------------------------------------------
---- Unix ----
In a Unix-like system (including MacOS), you can install the DRUGraph
software by uncompressing the tarball (tar -xzvf
drugraph-version.tar.gz) and running the usual stuff from the
drugraph-version directory:
cd drugraph-version
./configure
./make
./make install
This will install the software in your/default/bin/directory. To
install in a different directory run
./configure --prefix=path_to_install_directory
instead of just "./configure". For other configure options run:
./configure -h
You can uninstall the whole thing by running
./make uninstall
from the installation directory.
---- Windows (MinGW) ----
1) First of all, you have to download and install MinGW
(http://sourceforce.net/projects/mingw/files/latest/download?source=files)
During the installation, when it prompts you which packages to
install, select gcc, msys and mingw base. The other default options
are OK.
2) Download GNU Scientific Libraries (GSL), available from
http://www.gnu.org/software/gsl/. In my installation, I've used
version 1.15.
3) Launch MinGW console (Programs -> MinGW -> MinGW Shell or
C:\MinGW\msys\1.0\msys).
4) Unzip the contents of the GSL downloaded file under your msys home
which is at C:\MinGW\msys\1.0\home\user\ (it's important to perform
step 3 or you won't have the home directory).
5) In your msys console, cd into the gsl-15 folder and type the following:
./configure --prefix=/MinGW
make
make install
All these steps may take a while.
6) Untar the contents of rgraph under your msys home and type the following:
./configure make
[make install]
----------------------------------------------------------------------
USING DRUGraph
----------------------------------------------------------------------
The program takes as input a file containing a list of drug-drug
interactions with the following format:
d1 d2 0
d1 d3 1
d1 d4 1
...
This corresponds to a situation in which drug 1 (d1) and drug 2 (d2)
have an interaction of type 0, d1 and d3 have an interaction of type
1, and so on. Interaction types must be integer between 0 and K-1,
where K is the number of interaction types.
The program outputs a file predictions.dat with the following format:
d2 d3 0.50 0.20 0.30
d2 d4 0.15 0.55 0.30
...
This corresponds to a situation with three types of interactions
(K=3), and indicates that: drugs 2 and 3 have an interaction of type 0
with probability 0.50, of type 1 with probability 0.20, and of type 2
with probability 0.30; drugs 2 and 4 have an interaction of type 0
with probability 0.15, of type 1 with probability 0.55, and of type 2
with probability 0.30; and so on.
To see how everything works with a practical example, you can change
to the directory main/ in the installation directory and run drugraph
on the test network ddi_cokol_TRAIN.dat.
Command line parameters
----------------------------------------------------------------------
To run the program, type the following in the command line:
drugraph K net_file niterations seed
where:
* K is the number of types of interactions (for example, if
interactions can be antagonistic, additive or synergistic, then
K=3).
* net_file is the file containing the known interactions, and has the
format described above.
* niterations is the number of sampling iterations carried out by the
Metropolis algorithm.
* seed is the seed for the random number generator, and can be any
positive integer.
Running output
----------------------------------------------------------------------
After starting the program, the algorithm proceeds by: (i) determining
a convenient thinning step; (ii) thermalizing the sampler; (iii)
sampling. Please, be aware that each of these processes can take a
long time (even days) in networks larger than a few hundreds of drugs.
----------------------------------------------------------------------
CONTACT
----------------------------------------------------------------------
sees.lab@gmail.com

About

A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

Resources

Stars

1 star

Watchers

2 watching

Forks

Releases

Packages

Contributors

Languages