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<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>About - SDLDpred</title>
<link rel = "stylesheet" type = "text/css" href = "css/main.css" />
<style>
li{
width: 70%;
margin: 20px 15% 20px 15%;
font-size: 1.2em;
}
img{
max-width: 100%;
}
</style>
</head>
<body>
<div class = "section_header">
<center><p class="title">SDLDpred - Symptom-based Drugs of Lifestyle-related Diseases prediction</p></center>
</div>
<div class = "section_menu">
<center>
<table cellpadding="3px">
<tr class="nav">
<td class="nav"><a href="index.html" class="side_nav">Home</a></td>
<td class="nav"><a href="#" class="active">About</a></td>
<td class="nav"><a href="help.html" class="side_nav">Help</a></td>
<td class="nav"><a href="team.html" class="side_nav">Team</a></td>
</tr>
</table>
</center>
</div>
<!--<div class = "section_left"></div>-->
<div class = "section_middle">
<ul>
<li>
<b>SDLDpred</b> is a web-based tool for prediction of drugs of
lifestyle-related diseases from symptom data.
</li>
<li>
It used <i>unsupervised machine learning</i> models trained with <i>Bisecting
K-Means</i> algorithm using drug-symptom association data of <b>143
lifestyle-related diseases</b>, with focus on pulmonary and cardiovascular
diseases. A summary of the diseases are given in the following table.
<br/><br/>
<table class = "details" border = "0" id = "dtable">
<tr>
<th>Disease category</th>
<th>Number of diseases</th>
</tr>
<tr>
<td>Pulmonary diseases</td>
<td>67</td>
</tr>
<tr>
<td>Cardiovascular diseases</td>
<td>56</td>
</tr>
<tr>
<td>Musculoskeletal diseases</td>
<td>15</td>
</tr>
<tr>
<td>Diabetes</td>
<td>4</td>
</tr>
<tr>
<td>Obesity</td>
<td>1</td>
</tr>
<tr>
<th>Total</th>
<td><b>143</b></td>
</tr>
</table>
<center>
<b>List of diseases:</b> <a href="resource/data/disease_category.tsv">Download</a> [<i>TSV</i> file];
<b>List of drugs:</b> <a href="resource/data/drugs.tsv">Download</a> [<i>TSV</i> file];
<b>List of symptoms:</b> <a href="resource/data/symptoms.tsv">Download</a> [<i>TSV</i> file]
</center>
</li>
<li>
<b>Association datasets:</b>
<br/><br/>
<table class = "details" border = "0" id = "atable">
<tr>
<th>Association</th>
<th>Data source</th>
<th>Description</th>
<th>Download</th>
</tr>
<tr>
<td><b>Drug-disease associations</b></td>
<td>Comparative Toxicogenomics Database (CTD)</td>
<td>3159 associations; 1271 drugs; 143 diseases</td>
<td>
<b>Matrix <i>(TSV file)</i></b> <a href="resource/data/drug_disease_association_matrix.tsv">Download</a><br/>
<b>Edge-list <i>(TSV file)</i></b> <a href="resource/data/drug_disease_association_edgelist.tsv">Download</a>
</td>
</tr>
<tr>
<td><b>Symptom-disease associations</b></td>
<td>Human Symptom Disease Network (HSDN)</td>
<td>9466 associations; 305 symptoms; 143 diseases</td>
<td>
<b>Matrix <i>(TSV file)</i></b> <a href="resource/data/symptom_disease_association_matrix.tsv">Download</a><br/>
<b>Edge-list <i>(TSV file)</i></b> <a href="resource/data/symptom_disease_association_edgelist.tsv">Download</a>
</td>
</tr>
</table>
</li>
<li>
The <b>drug-symptom associations</b> were computed based on
(i) <i>cosine similarity</i> ( <i style="font-family: serif;">A<sub>drug-symptom<cosine></sub></i> ),
(ii) <i>Pearson's Correlation Coefficient</i> ( <i style="font-family: serif;">A<sub>drug-symptom<pearson></sub></i> ),
and
(iii) <i>Jacard similarity</i> ( <i style="font-family: serif;">A<sub>drug-symptom<jaccard></sub></i> )
between each drug and symptom represented as a vector of
disease-associations.
<center><img src="resource/about1.jpg" height="45px" width="auto" /></center>
<center><img src="resource/about2.jpg" height="50px" width="auto" /></center>
<center><img src="resource/about3.jpg" height="50px" width="auto" /></center>
<i style="font-family: serif;">dr<sub>i</sub></i> =
<i style="font-family: serif;">i<sup>th</sup></i> drug represented as a vector
of associations with the diseases,
<i style="font-family: serif;">s<sub>j</sub></i> =
<i style="font-family: serif;">j<sup>th</sup></i> symptom represented as a
vector of associations with the diseases,
<i style="font-family: serif;">||x||</i> = Magnitude of the vector
<i style="font-family: serif;">x</i>.<br/><br/>
The drug-symptom association dataset:
<table class = "details" border = "0" id = "dsatable">
<tr>
<td><i style="font-family: serif;">A<sub>drug-symptom<cosine></sub></i></td>
<td>
<a href="resource/data/drug_symptom_association_cosine_matrix.tsv">Download</a> [Matrix (TSV file)]<br/>
<a href="resource/data/drug_symptom_association_cosine_edge_list.tsv">Download</a> [Edge-list (TSV file)]
</td>
</tr>
<tr>
<td><i style="font-family: serif;">A<sub>drug-symptom<pearson></sub></i></td>
<td>
<a href="resource/data/drug_symptom_association_pearson_matrix.tsv">Download</a> [Matrix (TSV file)]<br/>
<a href="resource/data/drug_symptom_association_pearson_edge_list.tsv">Download</a> [Edge-list (TSV file)]
</td>
</tr>
<tr>
<td><i style="font-family: serif;">A<sub>drug-symptom<jaccard></sub></i></td>
<td>
<a href="resource/data/drug_symptom_association_jaccard_matrix.tsv">Download</a> [Matrix (TSV file)]<br/>
<a href="resource/data/drug_symptom_association_jaccard_edge_list.tsv">Download</a> [Edge-list (TSV file)]
</td>
</tr>
</table>
</li>
<li>
SDLDpred provides a <b>confidence score</b> for each predicted drug. It is
computed using the following equation.
<center><img src="resource/about4.jpg" height="60px" width="auto" /></center>
<i style="font-family: serif;">D</i> = random variable denoting the pairwise
euclidean distances between the drugs; <i style="font-family: serif;">e</i> =
euclidean distance of a predicted drug;
<i style="font-family: serif;">f<sub>dist</sub></i> = probability density
function of the random variable <i style="font-family: serif;">D</i>.
</li>
</ul>
<br/><hr/>
<p style="font-size:0.8em;text-align:center;">
Please contact Dr. Sudipto Saha (<a href="mailto:ssaha4@jcbose.ac.in">ssaha4@jcbose.ac.in</a>,
<a href="mailto:ssaha4@gmail.com">ssaha4@gmail.com</a>) regarding any further queries.
</p>
</div>
</body>
</html>