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<html xml:lang="en" xmlns="http://www.w3.org/1999/xhtml" lang="en"><head>
<title>BioFolD - Biomolecules, Folding and Disease</title>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
<meta name="Keywords" content="
stability, folding, disease, mutation, SNP,
I-Mutant, Meta-SNP, PhD-SNP, K-Fold, machine learning,
Emidio, Capriotti, Bioinformatics, Proteins
, University of Bologna, Bologna, unibo">
<meta name="Author" content="Emidio Capriotti">
<meta name="Generator" content="">
<link rel="stylesheet" href="pages/style.css"
type="text/css" title="BioFolD - Biomolecules, Folding and Disease"></head><body>
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<!-- Start main -->
<div id="main">
<br><br><br>
<!-- Start header -->
<div id="content"><img class="header" src="./pages/img/strubio.png" alt="BioFolD -
University of Bologna" align="center" /><br />
<br />
</div>
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<table class="navig">
<tbody><tr>
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<td class="cell"><a href="index.html">Home</a></td>
<td class="cell"><a href="pages/research.html">Research</a></td>
<td class="cell"><a href="pages/people.html">People</a></td>
<td class="cell"><a href="pages/resources.html">Resources</a></td>
<td class="cell"><a href="pages/papers.html">Publications</a></td>
<td class="cell"><a href="courses">Training</a></td>
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<br>
<p class="txtzone">
<br>
The BioFolD Unit is currently hosted at
the <a href="http://www.fabit.unibo.it/">Department of Pharmacy and Biotechnology (FaBiT)</a>, University of Bologna, Bologna, Italy (see below).
Computational resources are hosted on a server of the <a href="http://www.mathmodeling.hhu.de/" target=_blank>Institute for Mathematical Modeling of Biological Systems, University of Düsseldorf</a>.
The research activity of the unit is focused on the analysis of of genetic variants using sequence and structure information of proteins and nucleic acids. The main research topics are the developing of new methods for predicting the impact of non-synonymous variants, comparison and prediction of protein and RNA structure.
<br><br>
The final goal of our research activity is the understanding of the relationship between genomic variations and disease using large amount of data derived from high-throughput techniques.
We are interested to develop new disease-specific algorithms and new tools for the personal genomics and personalized medicine.
<br>
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