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    <loc>https://init.bio/blog/blog-post-title-one-h5zj6</loc>
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    <lastmod>2025-12-23</lastmod>
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      <image:title>Blog - Simplifying computational biology (for people building life sciences companies and products)</image:title>
      <image:caption>Figure 1. Four pillars of applied computational biology: subdisciplines that equate to recurring job functions in a company. Core Science Data Analysis Algorithm Development Software &amp; Data Engineering</image:caption>
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      <image:title>Blog - Simplifying computational biology (for people building life sciences companies and products)</image:title>
      <image:caption>Figure 2. These four job functions are distinct but sometimes overlap in practice. Separate areas of each circle indicate a recurring job function in a company. In practice, these domain areas and job functions can overlap, and this overlap is critical to developing a life sciences product using applied computational biology. (For example, someone doing much of the data analysis should also be able to participate in experimental design and have key scientific domain knowledge. Also, in an ideal world, everyone involved in the cross-functional team should be able to program and understand code, to varying extents.)</image:caption>
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    <loc>https://init.bio/blog/blog-post-title-two-63dr6</loc>
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    <lastmod>2023-04-17</lastmod>
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      <image:title>Blog - Lessons on computational biology teams from working in biotech</image:title>
      <image:caption>Here are some examples of decisions around computational biology / bioinformatics team building that I have encountered that ran into problems:</image:caption>
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      <image:title>Blog - Lessons on computational biology teams from working in biotech</image:title>
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    <image:image>
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      <image:title>Blog - Lessons on computational biology teams from working in biotech</image:title>
      <image:caption>Here are some examples of “bioinformatics done right” — teams I have worked with that were super productive and fun:</image:caption>
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      <image:title>Blog - Lessons on computational biology teams from working in biotech</image:title>
      <image:caption>An important theme that emerges from the above experiences is the need for complementarities. These can often fall across core biology / data analysis and software engineering / ML / algorithms, as described in this post. Best practices in collaborative development are also super important to get right early on (using source control tools like Github, regular check-ins and code reviews prior to merging branches, using project management tools like JIRA or Confluence to draft requirements and track issues, etc.).</image:caption>
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    <lastmod>2025-05-22</lastmod>
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