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<!DOCTYPE html>
<html lang="en" dir="ltr">
<head>
<meta charset="utf-8">
<title>UniDL4BioPep</title>
<link href='https://fonts.googleapis.com/css?family=Tangerine' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family==Rancho&effect=shadow-multiple' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family=Tangerine' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family=Tangerine' rel='stylesheet' type='text/css'>
<link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
<style>
.content {
max-width: 1000px;
margin: auto;
}
</style>
</head>
<body>
<div class="content">
<h1>UniDL4BioPep: A universal deep learning architecture for bioactive peptide prediction</h1>
<p style="font-size:70%">The webserver is the implementation of the paper "UniDL4BioPep: UniDL4BioPep: A universal deep learning architecture for binary classification in peptide bioactivity"</p>
<p style="font-size:80%">Quick output version: 1. Choose a model → 2. Input a peptide sequence</p>
<form action="{{url_for('predict')}}" method="post">
<label for="Model selection">Choose a model:</label>
<select name="Model selection" id="model1" required="required">
<option value="1">Logistic_Regression</option>
<option value="2">SVM</option>
<option value="3">Multilayer_Perceptron</option>
</select>
{# <input type="text" name="Model selection" placeholder="input model index, example:1" required="required">#}
<input type="text" name="Peptide_sequences" placeholder="peptide sequence input, example: ASD,QWE,APE" required="required" />
<button type="submit" class="btn btn-primary btn-block btn-large">Run</button>
</form>
<p style="color:cyan"> {{ prediction_text }} </p>
<br/>
<br/>
<p style="font-size:80%">Large-scale output version: 1. Prepare your files (xls, xlsx, fasta, or txt) and click “Choose File” for uploading → 2. Choose a model for classification → 3. Download the results. </p>
<form action="{{url_for('pred_with_file')}}" method="post" enctype = "multipart/form-data">
<label for="Model_selection">Choose a model:</label>
<select name="Model_selection" id="model2" required="required">
<option value="1">Logistic_Regression</option>
<option value="2">SVM</option>
<option value="3">Multilayer_Perceptron</option>
</select>
<br>
<label for="Peptide_sequences">Choose a file to upload</label>
<input type="file" name="Peptide_sequences" id="Peptide_sequences" accept=".xls,.xlsx,.txt,.fasta"/>
<button type="submit" class="btn btn-primary btn-block btn-large">Run</button>
</form>
<h4>Usage of the webserver:</h4>
<p style="font-size:80%"> Example for “Quick output version” : </p>
<p style="font-size:70%">1. Select “Antihypertensive” model for antihypertensive activity prediction. → → → 2. Insert a peptide or protein sequence, “VPP” → → → 3. Click “Run”→ → → 4. The result will be returned in seconds below the “Run” button</p>
<p style="font-size:70%">Notice: it also support multiple sequence at the same time. Just input as “VPP,IPP,CCL,AGR” (sequences are separated by comma, no space)</p>
<p style="font-size:80%"> Example for “Large-scale output version:” : </p>
<p style="font-size:70%">1. Prepare your xls, xlsx, txt or fasta files → → → 2. Upload the file through “Choose File” botton → → → 3. Click “Run” → → → 4. It will automatically download your results. </p>
<p style="font-size:70%">Notice: File preparation should follow the examples under this repository https://github.com/dzjxzyd/UniDL4BioPep_webserver/tree/main/Example%20uploading%20files </p>
<img src="static/css/Whole architecture.png" alt="Whole architecture" width="950" height="650" style="float:left">
</div>
</body>
</html>