Intern spotlight Nourah Salem
Who is Nourah Salem?
I am a master's degree student at Arizona State University, studying Health Informatics. Before that, I studied Computational Biology and Genomics, courses included Python and R programming and their applications in Biology.
Why did you decide to apply to intern at Systems Imagination?
After my undergraduate degree, I had the opportunity to work in the healthcare field for a year, where I fell in love with AI. I was working on a project where we would classify breast cancer scans collected from Mammography machines. This paved the path for me to infuse my Biology background with new techniques such as Deep Learning.
Besides being able to apply AI, ML, and Deep learning to the cancer field, I firmly believe that technology can bring new insights such as discovering new potential drugs for cancer treatment to the next generation of clinicians and researchers.
Tell us about your intern project?
Synthetic lethality (SL), is an important type of genetic interaction in which the synthesis of mutations in the cell can result in its death. This process can contribute to valuable insights into the target identification for the development of anticancer therapeutics. Tumor‐specific synthetic lethality became targeted in research to improve cancer therapy, although elucidating the synthetic lethality mechanism remains a challenge, because of the complexity of its influencing condition. Machine learning can assist in identifying these synthetic lethal gene pairs. Therefore, we worked on enhancing and developing a complete cascade of generating and qualifying the lethal candidate pairs.
For the project supervision, we have the privilege to be mentored by Kendyl Douglas and David Schneider. Apart from bringing industry insights, they always made themselves available for questions and encouraged us to try new ideas. Furthermore, Chris Yoo was always around to support us, update us with current innovations and opportunities, and teach us new things through the general group meetings and the arranged seminars.
What were a few of your biggest takeaways this summer?
It was a three-month internship packed full of knowledge, hands-on experience, and development of several skills. For instance, we looked at several datasets and went through their processing, implemented many algorithms, and explored literature. We were able to prepare the following datasets for training: Slorth, Synleth, Biogid, Gene Ontology and Slant. We used them to feed the following ML methods Neural Networks, Random Forest, Regression, and Support vector machine. Moreover, we made a network graph to extract node wise features for the pairs of genes to promote the training with. We applied NLP of the GO data to be able to find new features within the descriptive text.
I have also broadened my knowledge base in cancer genetics, built and optimized new algorithms, and worked on one of the largest hypergraphs in existence. It was an enjoyable experience and I can't thank each everyone enough for their guidance throughout my time at SII.
August 24, 2020