<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/44267?offset=30</link>
	<atom:link href="https://bioinformaticsonline.com/related/44267?offset=30" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models</guid>
	<pubDate>Wed, 22 Jan 2020 23:58:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40591/modelstudio-a-package-automates-the-explanation-of-machine-learning-predictive-models</link>
	<title><![CDATA[modelStudio: a package automates the explanation of machine learning predictive models]]></title>
	<description><![CDATA[<p>The&nbsp;<code>modelStudio</code>&nbsp;package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site.</p>
<p>It combines&nbsp;<strong>R</strong>&nbsp;with&nbsp;<strong>D3.js</strong>&nbsp;to produce plots and descriptions for various local and global explanations. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior.&nbsp;<code>modelStudio</code>&nbsp;is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.</p><p>Address of the bookmark: <a href="https://modeloriented.github.io/modelStudio/index.html" rel="nofollow">https://modeloriented.github.io/modelStudio/index.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/991/master-thesis-trans-membrane-topology-prediction-through-markov-based-decoders</guid>
	<pubDate>Wed, 17 Jul 2013 16:16:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/991/master-thesis-trans-membrane-topology-prediction-through-markov-based-decoders</link>
	<title><![CDATA[Master Thesis: Trans-membrane topology prediction through Markov based decoders]]></title>
	<description><![CDATA[<p dir="ltr"><span>Abstract:</span></p><p dir="ltr"><span></span><span>Background/Motivation: </span></p><p dir="ltr"><span>The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge &ndash;based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains.</span><span>&nbsp;</span></p><p dir="ltr"><span>Results: </span><span><span><span>In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods.</span></span></span><span>&nbsp;</span></p><p dir="ltr"><span><strong>Source Code</strong>: Matlab</span></p><p dir="ltr"><span></span><span>Conclusion/Implementation</span><span><span><span>: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various &nbsp;Markov decoder , and assignment of the membrane helix boundaries was apparent.</span></span></span></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/991" length="161792" type="application/vnd.ms-powerpoint" />
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/40770/scientist-bioinformatics-positions</guid>
  <pubDate>Thu, 30 Jan 2020 06:53:40 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist Bioinformatics Positions]]></title>
  <description><![CDATA[
<p>Bioinformatics-Multi_Omics_Integration</p>

<p>https://www.researchgate.net/job/939073_Senior_Scientist_Bioinformatics-Multi_Omics_Integration</p>

<p> <br />Senior_Scientist_Bioinformatics-Transcriptomics_Analysis     </p>

<p>https://www.researchgate.net/job/939075_Senior_Scientist_Bioinformatics-Transcriptomics_Analysis-Belgium_France_Switzerland_The_Netherlands</p>

<p>Senior Scientist Bioinformatics - Network Analytics</p>

<p>https://www.researchgate.net/job/939070_Senior_Scientist_Bioinformatics-Network_Analytics_Belgium_France_Switzerland_the_Netherlands</p>

<p>Team Leader Bioinformatics Data Sciences - Mechelen, Belgium</p>

<p>https://www.researchgate.net/job/938787_Team_Leader_Bioinformatics_Data_Sciences-Mechelen_Belgium</p>
]]></description>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/41394/ngsymposium-in-computational-biology</guid>
  <pubDate>Mon, 09 Mar 2020 06:00:30 -0500</pubDate>
  <link></link>
  <title><![CDATA[NGSymposium in Computational Biology]]></title>
  <description><![CDATA[
<p>We have a great pleasure to invite you to the NGSymposium in Computational Biology to celebrate the 5th anniversary of the NGSchool Summer Schools. This international conference will make way for exchanging knowledge and experiences between experienced and early-stage researchers as well as bioinformaticians. The meeting will be held on 31.07 - 1.08.2020 in Warsaw. It will be a satellite event to the #NGSchool2020: Statistical Learning in Genomics. It will cover a wide range of topics from basic and applied biomedical sciences: bioinformatics, genomics, transcriptomics, computational biology, Machine Learning.</p>

<p>Registration of active participants will be open from February, 27 12 PM CET to April 17, 23:59 CET. In registration forms you will be asked for providing us with some basic information about yourself. You will also be able to submit your abstract. You can save your registration form after filling it partially and come back later to supply more data e.g. upload an abstract. Your registration will be completed only with the payment of the registration fee reaching our accounts - please make sure to transfer the money in advance!</p>

<p>Registration of passive participants will be open after closing of registration of active participants.</p>

<p>Details an registration: https://ngschool.eu/conference/</p>
]]></description>
</item>

</channel>
</rss>