A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/prot.26144">the original URL</a>. The file type is <code>application/pdf</code>.
SCLpred‐MEM : subcellular localization prediction of membrane proteins by Deep N‐to‐1 Convolutional Neural Networks
<span title="2021-05-13">2021</span>
<i title="Wiley">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fkowqlvuffe5lnyfwblj3fcp7i" style="color: black;">Proteins: Structure, Function, and Bioinformatics</a>
</i>
Preserved Fulltext
The knowledge of the subcellular location of a protein is a valuable source of information in genomics, drug design and various other theoretical and analytical perspectives of Bioinformatics. Due to the expensive and time-consuming nature of experimental methods of protein subcellular location determination, various computational methods have been developed for subcellular localization prediction. We introduce "SCLpred-MEM", an ab initio protein subcellular localization predictor, powered by
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/prot.26144">doi:10.1002/prot.26144</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33983651">pmid:33983651</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qgte7amdjfd3fkggzfnodywrhq">fatcat:qgte7amdjfd3fkggzfnodywrhq</a>
</span>
more »
... ensemble of Deep N-to-1 Convolutional Neural Networks (N1-NN) trained and tested on strict redundancy reduced datasets. SCLpred-MEM is available as a web-server predicting query proteins into two classes, membrane and non-membrane proteins. SCLpred-MEM achieves a Matthews correlation coefficient (MCC) of 0.52 on a strictly homology-reduced independent test set and 0.62 on a less strict homology reduced independent test set, surpassing or matching other state-of-the-art subcellular localization predictors. This article is protected by copyright. All rights reserved.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210718031141/https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/prot.26144" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/14/93/1493c10b08864893c580c34a65b7ed32f5a1cfdc.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/prot.26144">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
wiley.com
</button>
</a>