<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Machine Learning | Dr. Hu Kaining</title>
    <link>https://kaininghu.github.io/tag/machine-learning/</link>
      <atom:link href="https://kaininghu.github.io/tag/machine-learning/index.xml" rel="self" type="application/rss+xml" />
    <description>Machine Learning</description>
    <generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Sun, 01 Jan 2023 00:00:00 +0000</lastBuildDate>
    <image>
      <url>https://kaininghu.github.io/media/icon_hu9246187806002616642.png</url>
      <title>Machine Learning</title>
      <link>https://kaininghu.github.io/tag/machine-learning/</link>
    </image>
    
    <item>
      <title>EANMD</title>
      <link>https://kaininghu.github.io/project/eanmd/</link>
      <pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate>
      <guid>https://kaininghu.github.io/project/eanmd/</guid>
      <description>&lt;p&gt;EANMD is a machine learning tool that uses XGBoost to predict alternative splicing events that trigger nonsense-mediated mRNA decay (NMD). It integrates sequence features and evolutionary conservation to classify NMD-targeted isoforms.&lt;/p&gt;
&lt;p&gt;Key features:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;XGBoost-based classification model&lt;/li&gt;
&lt;li&gt;Prediction of AS-NMD events from transcript features&lt;/li&gt;
&lt;li&gt;Integration of evolutionary and sequence-based features&lt;/li&gt;
&lt;li&gt;Applied in neurodevelopmental disorder research&lt;/li&gt;
&lt;/ul&gt;
</description>
    </item>
    
  </channel>
</rss>
