As soon as I heard about the hackathon I was very interested in learning more about bioinformatics. Additionally, I knew that the Nobel Prize in Chemistry for 2020 was given to 2 women for developing a method for gene editing. The original idea was that since I have done a course on data science, I will try to explore applications of machine learning in this field. It is very important to understand terminology to know how to do data pre-processing before running the model. The resources were spread across different websites like Google, Nature, Youtube, Springer, GitHub, and I was forced to oscillate between the different platforms. This made me feel the need for a one-stop shop info hub for everything related to Gene Editing! Cue - "Cas9 Einstein."

What it does

It is a replica of Google, but instead of redirecting you to other websites all the information is embedded in the website, so you can read your article journal there itself. It is directly linked to your GitHub so you can search for rrepositories directly, get the latest News, watch videos, see images, everything under one roof. Additionally, a research paper was read and its corresponding github repo was explored. The results were replicated.

How I built it

I built the website using HTML and CSS. I identified what was the flow I followed when I attempted to learn about gene editing and bioinformatics to ensure all the tabs cover the necessary information. I also ran a GitHub repo corresponding to an article I found interesting using CNNs for guide RNA activity prediction.

Challenges I ran into

Was only the second time I'm working with CSS so it was difficult. Also, have never explored Deep Learning before, so this was a challenge to understand what is CNN, what is convolution and how to use GPU on Google Colab.

Accomplishments that I'm proud of

Firstly I have learnt so much about gene-editing and deep learning because of this project. I am super proud of having a fully function website and successfully running the model! I can now change the parameters of the deep learning model to see how the accuracy of the model varies.

What I learned

Delving into new and growing fields of gene editing and deep learning, and working in their intersection was a great learning curve. Lots of terminology needed to be absorbed in the past couple days, in both gene-editing and deep learning.

What's next for Cas9 Einstein

  • Using Google for Life Sciences API provided by Google Cloud for Genome analysis.
  • Working on improving the Deep Leaning model by changing parameters or adding more layers.
  • Work with a professor who is doing research in this field
  • Understand the moral and ethical concerns behind gene editing.
  • Introduce filters for images, videos, articles to further narrow down search

CNN-SVR Research Paper

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