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CAIN: Concordia Artificial Immune Network for B-cell Selection

Disclosure

This repo is a work in progress and results from this algorithm were not validated on ground-truth data.

Purpose

CAIN simulates B-cell selection and an immune response following the introduction of a foreign pathogenic antigen. This algorithm is only applicable to antigens with linear epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods according to the Point Accepted Mutation (PAM) matrix. The PAM1 and PAM250 matrices text file are provided. If you wish to use different versions, please place the files in the Resources directory and alter the code chunk in the Bcell_Selection.py file.

Following B-cell selection, an immune response is performed using game-theory concepts to run a 1x1 matrix game between each individual of the lymphocyte and antigen populations. The "loser" of each game loses an individual from their respective population and visa versa for the "winner".

Dependencies

Please install these module versions prior to using CAIN.

  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3

Installation

To clone the repository, open the command terminal and set your working directory.

Next, run the following command:

$ git clone https://github.com/phicks22/CAIN

Selection and Response

First: Set your root directory in the Arg_Parser.py file at the very top. Next, run the following command in the terminal, setting each argument per the desired parameters.

$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10

Required arguments:

  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations

Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.

Unsupervised Parameter Clustering

  1. To collect your data, uncomment the block section For Large Data Collection and set the disired ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.

About

An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.

Topics

artificial-immune-systembioinformaticsimmunologysystems-biology

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __re = new RegExp('^' + "github\\.com" + '

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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
About
An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
Topics
artificial-immune-systembioinformaticsimmunologysystems-biology
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
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injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
About
An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
Topics
artificial-immune-systembioinformaticsimmunologysystems-biology
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
About
An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
Topics
artificial-immune-systembioinformaticsimmunologysystems-biology
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
About
An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
Topics
artificial-immune-systembioinformaticsimmunologysystems-biology
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
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try {
var __m = "*";
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Skip to content
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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
About
An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
Topics
artificial-immune-systembioinformaticsimmunologysystems-biology
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
About
An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
Topics
artificial-immune-systembioinformaticsimmunologysystems-biology
Resources
Readme
Activity
Stars
2 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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CAIN: Concordia Artificial Immune Network for B-cell Selection
Disclosure
This repo is a work in progress and results from this algorithm were not validated on ground-truth data.
Purpose
CAIN simulates B-cell selection and an immune response following the introduction of a
foreign pathogenic antigen.
This algorithm is only applicable to antigens with linear
epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods
according to the Point Accepted Mutation (PAM) matrix.
The PAM1 and PAM250 matrices text file are provided.
If you wish to use different versions, please place the files in the Resources
directory and alter the code chunk in the Bcell_Selection.py file.
Following B-cell selection, an immune response is performed using game-theory concepts to
run a 1x1 matrix game between each individual of the lymphocyte and antigen populations.
The "loser" of each game loses an individual from their respective population and visa versa
for the "winner".
Dependencies
Please install these module versions prior to using CAIN.
  • Python 3.0+
  • Numpy 1.21.4
  • Pandas 1.3.4
  • tqdm 4.62.3
Installation
To clone the repository, open the command terminal and set your working directory.
Next, run the following command:
$ git clone https://github.com/phicks22/CAIN
Selection and Response
First: Set your root directory in the Arg_Parser.py file at the very top.
Next, run the following command in the terminal, setting each argument per the desired parameters.
$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10
Required arguments:
  • -e: Antigen linear epitope. Example: ARIKDDCGHAI
  • -n: Number of individuals in each B-cell population
  • -d: Antigen division rate
  • -p: Number of B-cell populations
  • -ex: Number of exchange iterations that occur during B-cell selection
  • -r: Number of immune response iterations
Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.
Unsupervised Parameter Clustering
  1. To collect your data, uncomment the block section For Large Data Collection and set the disired
ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
  2. Load the data into Parameter_Clustering.py and choose the desired clustering method.
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An artificial immune network to simulate B-cell clonal selection and perform an agent-based game immune response.
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artificial-immune-systembioinformaticsimmunologysystems-biology
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