https://touchingfish.top/tags/feature-engineering/Recent content in Feature-Engineering on TouchingFish.topHugozh-cnThu, 24 Jan 2019 00:00:00 +0000https://touchingfish.top/2019/bioseq-preprocess/Thu, 24 Jan 2019 00:00:00 +0000https://touchingfish.top/2019/bioseq-preprocess/<p>随着生物信息学的快速发展，DNA 序列数据呈爆炸式增长。这些序列蕴含着丰富的生物学信息——然而对处理数值数据的机器学习模型来说，原始的 DNA 序列不过是一串字符，<code>ACGTACG...</code>，无法直接作为输入。</p> <p>将 DNA 序列转化为机器学习模型能够理解的数值矩阵形式，成为一个关键步骤。</p> <p>（本文明写 <em>E.Coli</em> 启动子序列识别，实则记录一种通用的序列转换思路）</p> <h2 id="数据预览">数据预览</h2> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="cl"><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> </span></span><span class="line"><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> </span></span><span class="line"><span class="cl"> </span></span><span class="line"><span class="cl"><span class="n">url</span> <span class="o">=</span> <span class="s1">&#39;https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/promoters.data&#39;</span> </span></span><span class="line"><span class="cl"><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Class&#39;</span><span class="p">,</span> <span class="s1">&#39;id&#39;</span><span class="p">,</span> <span class="s1">&#39;Sequence&#39;</span><span class="p">]</span> </span></span><span class="line"><span class="cl"><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="n">names</span><span class="p">)</span> </span></span><span class="line"><span class="cl"> </span></span><span class="line"><span class="cl"><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> </span></span><span class="line"><span class="cl"><span class="nb">print</span><span class="p">(</span><span class="s2">&#34;</span><span class="se">\n</span><span class="s2">&#34;</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">)</span> </span></span></code></pre></div><p>输出：</p> <pre tabindex="0"><code>Class + id S10 Sequence \t\ttactagcaatacgcttgcgttcggtggttaagtatgtataat... Name: 0, dtype: object &lt;bound method NDFrame.head of Class id Sequence 0 + S10 \t\ttactagcaatacgcttgcgttcggtggttaagtatgtataat... 1 + AMPC \t\ttgctatcctgacagttgtcacgctgattggtgtcgttacaat... 2 + AROH \t\tgtactagagaactagtgcattagcttatttttttgttatcat... 3 + DEOP2 \taattgtgatgtgtatcgaagtgtgttgcggagtagatgttagaa... 4 + LEU1_TRNA \ttcgataattaactattgacgaaaagctgaaaaccactagaatgc... .. ... ... ... 101 - 799 \t\tcctcaatggcctctaaacgggtcttgaggggttttttgctga... 102 - 987 \t\tgtattctcaacaagattaaccgacagattcaatctcgtggat... 103 - 1226 \t\tcgcgactacgatgagatgcctgagtgcttccgttactggatt... 104 - 794 \t\tctcgtcctcaatggcctctaaacgggtcttgaggggtttttt... 105 - 1442 \t\ttaacattaataaataaggaggctctaatggcactcattagcc... [106 rows x 3 columns]&gt; </code></pre><h2 id="序列的提取与分割">序列的提取与分割</h2> <p>DataFrame 是由多个 Series 组成的，每个 Series 代表 DataFrame 的一列。</p>