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1.2: Final Project - Introduction to Research In Computational Biology

  • Page ID
    40907
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    Final project goals

    An important component of being a computational biologist is the ability to carry out independent research in the area. The skills for a successful researcher differ from one person to the next, but in the process of teaching this course, we have identified several aspects that are all needed, and laid out activities for a term-long project, that enable students to carry out their independent research.

    The project mirrors real world scientific process: come up with an idea → frame it → propose it→ revise it → carry it out → present your results. Students are expected to think critically about their own project, and also evaluate peer research proposals, and lastly respond to feedback from their peers.

    Students are expected to use real data and present their results in conference format. The ultimate goal is publishable research. Students are encouraged to talk with the course staff while formulating a final project idea, look head through the various chapters and modules, and get an idea of what areas will interest you most.

    Final project milestones

    Instead of waiting until the end of the term to begin brainstorming or provide feedback, we begin project activities with the first problem set, to identify problems of interest and types of projects, find partners, speak with current students and postdocs in computational biology that can serve as mentors, and lay out a research plan in the style of an NIH proposal to identify potential pitfalls early and address them or work around them before they become a bottleneck.

    By setting up several incremental progress milestones throughout the term, coupled with mentoring and feedback throughout the semester, we have achieved consistent progress in previous years, which can be useful to students taking on a new project at any stage of their career. Research projects from this course in the past have been used as the starting point for a published paper, have led to Masters and PhD theses, and earned awards both academically and in conferences.

    The timeline for the final project is as follows:
    1. Set-up: a brief overview of your experience and interest. Due 9/29
    2. Brainstorming: a list of initial project ideas and partners. Due 10/6
    3. Proposal: submit a project proposal in the form of an NIH proposal. Due 10/20

    4. Proposal presentation: present slides to class and mentors on the proposal. Due 10/23 5. Review: review and critique 3 peer proposals. Due 10/30
    6. Midterm Progress Report: write outline of final report. Due 11/19
    7. Final Project Report: write report in conference paper format. Due 12/6

    8. Final Class Presentation: 10min conference talk. Due 12/10

    There will be Friday mentoring sessions before each portion of the final project is due, and you are encouraged to find a mentor at the first few sessions who is actively interested in your project and could help you more frequently. The mentoring sessions can be helpful in identifying if unexpected results are the result of a bug or are instead a discovery.

    Make sure you start working on the project even while waiting for peer reviews, so that you will have 4-5 weeks to complete the research itself.

    Project deliverables

    The final project will include the following two deliverables:

    1. A written presentation, due Mon at 8pm, last week of classes. The written presentation can contain the following elements:

      • Who did what (to reflect trend in publications)
      • The overall project experience
      • Your discoveries
      • What you learned from the experience (introspection)

    2. An oral presentation, due Thursday after the written presentation. This allows students three days to prepare the oral presentation.

    Project grading

    Selecting a project that will be successful can be difficult. To help students optimize for a successful project, we let them know in advance the grading scheme, designed to maximize the project impact by being orig- inal, challenging, and relevant to the field, but of course the grade is ultimately dependent on the overall achievement and the clarity of presentation.

    Briefly, the grading equation for the final project is:

    min(O,C,R)×A+P

    where

    Originality - unoriginal computational experiments don’t get published

    Challenge - the project needs to be sufficiently difficult

    Relevance - it needs to be from biology, can’t just reuse something from another field

    Achievement - if you don’t accomplish anything you won’t get a good grade

    Presentation - even if you’ve achieved a good project you have to be able to present it so everyone knows that, and make it look easy. The presentation should show how the project is O, C, and R.

    Originality, Challenge, Relevance are each out of 5 points, Achievement and Presentation are each out of 10.


    This page titled 1.2: Final Project - Introduction to Research In Computational Biology is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Manolis Kellis et al. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.