<p>It was a short course host by Department of Animal Science at University of California, Davis. The instructors include Drs. Hao Cheng (University of California Davis), Rohan Fernando (Iowa State University), James Chen (Virginia Tech), and Daniel Runcie (University of California, Davis). Several scholarships are available to cover registration and travel expenses for graduate students who attend a U.S. institution. Topics: Learn to code for the genome to phenome analysis from scratch. The following topics were covered: Genomic Prediction 1) Mixed-effects models with polygenic, maternal, and permanent environment effects 2) Iterative methods for solving linear systems 3) GBLUP and Bayesian alphabet 4) Variance component estimation 5) Multi-environment trials Phenomics 6) Image matrix manipulation 7) Computer vision algorithms and applications. The workshop will provided an introduction to programming and how to use it to code algorithms listed above from scratch for genomics to phenomics analyses. The course consists of lectures and practical components with hands-on exercises.</p>


A 5-day short course focusing on modern programming techniques for genome to phenome analysis.

Modern Programming in Genome to Phenome

<p>The workshop was a one-day event held in conjunction with the 2023 ADSA Annual Meeting in Ottawa, Canada. It was open to all ADSA Annual Meeting participants, including graduate students, faculty members, and industry professionals. The candidate led a two-hour session on "Three Types of Risk in Model Evaluation." This session employed multiple simulation examples to elucidate common pitfalls in model evaluation, covering topics such as the rationale for partitioning data into multiple subsets, the model selection process during hyperparameter tuning, and the optimistic bias that can arise when splitting data without considering experimental design. Additionally, the session explored potential solutions to mitigate these risks in model evaluation. Python code was provided during the session to demonstrate key concepts, enabling attendees to replicate the results.</p>


It is a two-hour workshop emphasizing on the common risks found in model evaluation.

Three Types of Risk in Model Evaluation

<h2>Course Objectives</h2>
<p>By the end of this course, you will be able to:</p>
<ul>
<li>Utilize data patterns to identify potential health risks and disease indicators.</li>
<li>Use computational tools to formulate and answer scientific questions.</li>
<li>Communicate discoveries through effective visualization.</li>
</ul>
<h2>What You Need to Bring to the Class (and Lab)</h2>
<p><img src="/teaching/apsc-3984/lectures/l1/1.png" alt="01"></p>
<h2>This Course Covers a Wide Range of Applications</h2>
<p><img src="/teaching/apsc-3984/lectures/l1/2.png" alt="02"></p>
<h3>Applications in This Course</h3>
<ul>
<li>Chick growth monitoring</li>
<li>Pig reproductive wellness</li>
<li>Dairy cow lactation and milk quality</li>
<li>Heifer spatial behavior</li>
<li>Canine aging and wellness</li>
</ul>
<h3>Skills You'll Develop</h3>
<ul>
<li>Data management and organization</li>
<li>Statistical analysis fundamentals</li>
<li>Data visualization techniques</li>
<li>Python programming basics</li>
<li>Real-world data analytics</li>
</ul>
<h2>This Course Required No Coding Experience</h2>
<p>But you need to have a high-level understanding to input <strong>meaningful prompts</strong>!</p>
<p><img src="/teaching/apsc-3984/lectures/l1/3.png" alt="0"></p>
<p>Continue with the next prompt...
<img src="/teaching/apsc-3984/lectures/l1/4.png" alt="0"></p>
<h2>Data Science in Livestock Farming</h2>
<p>Data-based animal management has been a trend to improve the monitoring accuracy and resolution (time and space) in many practical farming scenarios.</p>
<p><img src="/teaching/apsc-3984/lectures/l1/5.png" alt="0"></p>
<p>Effectively <strong>managing</strong>, <strong>interpreting</strong>, and <strong>expressing</strong> data are key components of the next generation of livestock farming.</p>
<h2>Ready to Start?</h2>
<p>Click the button below to dive into the first module!</p>


1. Descriptive Statistics

2. Pivot Tables

3. Functions

4. Case Study

5. Visualization

6. Multifacet Visualization

I. Excel Basics

This course is an introduction to data-based animal management for undergraduate students with no prior coding experience.

APSC-3984: Data-Based Animal Management

Three Types of Risk in Model Evaluation

Modern Programming in Genome to Phenome